--------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\justi\Desktop\replication file\esarey-schwbay.log
  log type:  text
 opened on:  22 Jun 2016, 20:06:10

. 
. *********************
. * basic summaries and correlations
. *********************
. 
. 
. * load in the data
. clear all

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * recode the DV
. rename icrg_corr icrg_corr_o

. gen icrg_corr = 6 - icrg_corr_o
(292 missing values generated)

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. cor L.icrg_corr press3_inverse pres_new pers_lower
(obs=1,487)

             |        L.                           
             | icrg_c~r press3~e pres_new pers_l~r
-------------+------------------------------------
   icrg_corr |
         L1. |   1.0000
press3_inv~e |  -0.6221   1.0000
    pres_new |   0.3409  -0.4101   1.0000
  pers_lower |   0.1472  -0.1567   0.0158   1.0000


. 
. cor cpi_ti icrg_corr wb_corr
(obs=949)

             |   cpi_ti icrg_c~r  wb_corr
-------------+---------------------------
      cpi_ti |   1.0000
   icrg_corr |  -0.8600   1.0000
     wb_corr |   0.9779  -0.8718   1.0000


. 
. estpost sum cpi_ti icrg_corr wb_corr press3_inverse pres_new pers_lower pctwomen fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new
> !=1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
      cpi_ti |      1029       1029   5.209951   5.483313   2.341648         .4         10    5361.04 
   icrg_corr |      1494       1494   2.575608   1.768986   1.330032          0          6   3847.958 
     wb_corr |       889        889    .425255   1.106565   1.051934    -1.4581   2.590772   378.0517 
press3_inv~e |      1315       1315  -31.09734   267.1869   16.34585        -75         -3     -40893 
    pres_new |      1494       1494   .4123159   .2424738   .4924163          0          1        616 
  pers_lower |      1489       1489   4.855608    15.0161   3.875061          1         13       7230 
    pctwomen |      1494       1494   15.73072   102.7401   10.13608          0       46.4    23501.7 
      fh_neg |      1494       1494  -2.093039   1.087453    1.04281         -5         -1      -3127 
     log_gdp |      1494       1494   8.566918   2.198586   1.482763   4.795258   11.46363   12798.98 
pct_protes~t |      1494       1494   23.32007    716.529   26.76806          0         91   34840.19 
trade_impexp |      1482       1482   78.64887   1477.208   38.43446   13.75305    280.361   116557.6 
       wecon |      1486       1486   1.589502   .4239705   .6511302          0          3       2362 

. 
. esttab .  using summary.rtf, cells("mean(fmt(3)) sd(fmt(3)) count(fmt(3)) min(fmt(3)) max(fmt(3))") noobs coeflabels(cpi_ti "TI CPI" icrg_
> corr "ICRG" wb_corr "WBGI" press3_inverse "FH press freedom" pres_new "presidentialism" pers_lower "personalism" pctwomen "% women in lowe
> r house" fh_neg "FH freedom score" log_gdp "log GDP per capita" pct_protestant "% protestant" trade_impexp "trade imbalance (% of GDP)" we
> con "women's economic rights") noabbrev nonum wrap gaps varwidth(25) align(r) replace
(output written to summary.rtf)

. 
. 
. ***************************************************************************
. * look at the "culture of corruption"
. * effect of women in the context of past corruption levels
. ***************************************************************************
. 
. * load in the data
. clear all

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. ****
. * Transparency International Measure
. ****
. 
. * recode the DV
. rename cpi_ti cpi_ti_o

. gen cpi_ti = 10 - cpi_ti_o
(739 missing values generated)

. 
. * scatterplot: Transparency International Measure
. twoway (scatter cpi_ti pctwomen if l.cpi_ti<=5 & l.cpi_ti!=. & exclude_new!=1) (lfit cpi_ti pctwomen if l.cpi_ti<=5 & l.cpi_ti!=. & exclud
> e_new!=1), title("Low Prior Corruption") xtitle("% Women in Lower House") ytitle("TI Corruption Perception Index") legend(label(1 "TI CPI"
> ) label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6 7 8 9 10) scheme(s2mono)

. graph export ti-lag-lo.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-lag-lo.emf written in Enhanced Metafile format)

. twoway (scatter cpi_ti pctwomen if l.cpi_ti>5 & l.cpi_ti!=. & exclude_new!=1) (lfit cpi_ti pctwomen if l.cpi_ti>5 & l.cpi_ti!=. & exclude_
> new!=1), title("High Prior Corruption") xtitle("% Women in Lower House") ytitle("TI Corruption Perception Index") legend(label(1 "TI CPI")
>  label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6 7 8 9 10) scheme(s2mono)

. graph export ti-lag-hi.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-lag-hi.emf written in Enhanced Metafile format)

. 
. qui reg cpi_ti l.cpi_ti pctwomen if exclude_new!=1

. unique country if e(sample)
Number of unique values of country is  76
Number of records is  943

. tab year if e(sample)

       Year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1996 |         35        3.71        3.71
       1997 |         39        4.14        7.85
       1998 |         42        4.45       12.30
       1999 |         61        6.47       18.77
       2000 |         58        6.15       24.92
       2001 |         57        6.04       30.97
       2002 |         66        7.00       37.96
       2003 |         70        7.42       45.39
       2004 |         74        7.85       53.23
       2005 |         75        7.95       61.19
       2006 |         74        7.85       69.03
       2007 |         73        7.74       76.78
       2008 |         74        7.85       84.62
       2009 |         73        7.74       92.36
       2010 |         72        7.64      100.00
------------+-----------------------------------
      Total |        943      100.00

. 
. gen lagdum = .
(2,002 missing values generated)

. replace lagdum = 0 if l.cpi_ti<=5 & l.cpi_ti!=.
(460 real changes made)

. replace lagdum = 1 if l.cpi_ti>5 & l.cpi_ti!=.
(695 real changes made)

. gen womXlagdum = pctwomen*lagdum
(850 missing values generated)

. reg cpi_ti pctwomen lagdum womXlagdum if(exclude_new!=1)

      Source |       SS           df       MS      Number of obs   =       943
-------------+----------------------------------   F(3, 939)       =   1351.36
       Model |  4186.09081         3   1395.3636   Prob > F        =    0.0000
    Residual |  969.577756       939  1.03256417   R-squared       =    0.8119
-------------+----------------------------------   Adj R-squared   =    0.8113
       Total |  5155.66857       942  5.47310889   Root MSE        =    1.0162

------------------------------------------------------------------------------
      cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    pctwomen |  -.0873167   .0044939   -19.43   0.000     -.096136   -.0784974
      lagdum |   2.323574   .1449497    16.03   0.000     2.039111    2.608037
  womXlagdum |    .069811   .0070806     9.86   0.000     .0559154    .0837066
       _cons |   4.552473   .1114931    40.83   0.000     4.333669    4.771277
------------------------------------------------------------------------------

. 
. xtunitroot fisher cpi_ti if exclude_new!=1, trend pperron lags(1)
(739 missing values generated)

Fisher-type unit-root test for cpi_ti
Based on Phillips-Perron tests
-------------------------------------
Ho: All panels contain unit roots           Number of panels       =     76
Ha: At least one panel is stationary        Avg. number of periods =  13.54

AR parameter:    Panel-specific             Asymptotics: T -> Infinity
Panel means:     Included
Time trend:      Included
Newey-West lags: 1 lag
------------------------------------------------------------------------------
                                  Statistic      p-value
------------------------------------------------------------------------------
 Inverse chi-squared(152)  P       281.8740       0.0000
 Inverse normal            Z        -2.1230       0.0169
 Inverse logit t(384)      L*       -3.8715       0.0001
 Modified inv. chi-squared Pm        7.4488       0.0000
------------------------------------------------------------------------------
 P statistic requires number of panels to be finite.
 Other statistics are suitable for finite or infinite number of panels.
------------------------------------------------------------------------------

. xtunitroot fisher cpi_ti if exclude_new!=1, trend dfuller lags(1)
(739 missing values generated)

Fisher-type unit-root test for cpi_ti
Based on augmented Dickey-Fuller tests
--------------------------------------
Ho: All panels contain unit roots           Number of panels       =     76
Ha: At least one panel is stationary        Avg. number of periods =  13.54

AR parameter: Panel-specific                Asymptotics: T -> Infinity
Panel means:  Included
Time trend:   Included
Drift term:   Not included                  ADF regressions: 1 lag
------------------------------------------------------------------------------
                                  Statistic      p-value
------------------------------------------------------------------------------
 Inverse chi-squared(152)  P       319.6473       0.0000
 Inverse normal            Z        -2.7942       0.0026
 Inverse logit t(369)      L*       -5.0847       0.0000
 Modified inv. chi-squared Pm        9.6152       0.0000
------------------------------------------------------------------------------
 P statistic requires number of panels to be finite.
 Other statistics are suitable for finite or infinite number of panels.
------------------------------------------------------------------------------

. 
. 
. * check proportion of cases with l.cpi_ti > 5
. qui reg cpi_ti l.cpi_ti  if exclude_new!=1

. gen highti = .
(2,002 missing values generated)

. replace highti = 0 if l.cpi_ti<5 & l.cpi_ti!=. & e(sample)
(427 real changes made)

. replace highti = 1 if l.cpi_ti>=5 & l.cpi_ti!=. & e(sample)
(516 real changes made)

. sum highti

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      highti |        943    .5471898    .4980323          0          1

. 
. 
. ice cpi_ti pctwomen fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1994, seed(123456) m(50) saving(wb_imputed, repla
> ce) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,016       50.75       50.75
          1 |        228       11.39       62.14
          2 |          5        0.25       62.39
          . |        753       37.61      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | cpi_ti pctwomen fh_neg log_gdp pct_protestant
            |         | trade_impexp
trade_imp~p | regress | cpi_ti pctwomen fh_neg log_gdp pct_protestant wecon
     cpi_ti | regress | pctwomen fh_neg log_gdp pct_protestant trade_impexp
            |         | wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. qui mi xeq: sort countryid year; by countryid: gen lagXwomen = l.cpi_ti * pctwomen

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg cpi_ti l.cpi_ti pctwomen lagXwomen fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_n
> ew!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3368
                                                Largest FMI       =     0.6106
                                                Complete DF       =       1143
DF adjustment:   Small sample                   DF:     min       =     103.57
                                                        avg       =     411.98
                                                        max       =     948.27
Model F test:       Equal FMI                   F(  32, 1087.0)   =     300.45
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .5825146   .0388221    15.00   0.000     .5058255    .6592038
               |
      pctwomen |  -.0303404   .0062525    -4.85   0.000    -.0426327   -.0180481
     lagXwomen |   .0053126   .0012219     4.35   0.000     .0029081     .007717
        fh_neg |  -.1984829    .039235    -5.06   0.000    -.2755335   -.1214322
       log_gdp |  -.3649319   .0440323    -8.29   0.000    -.4515212   -.2783427
pct_protestant |  -.0024491   .0012444    -1.97   0.049    -.0048924   -5.82e-06
  trade_impexp |  -.0006665   .0006714    -0.99   0.321    -.0019845    .0006515
         wecon |  -.0168448    .049729    -0.34   0.735    -.1146355    .0809458
               |
          year |
         1995  |  -.2614512    .183491    -1.42   0.157    -.6253383    .1024359
         1996  |  -.1078676   .1724107    -0.63   0.533    -.4491933    .2334581
         1997  |   .0051638   .1576594     0.03   0.974    -.3061975    .3165251
         1998  |  -.0183751    .153578    -0.12   0.905    -.3214456    .2846953
         1999  |  -.0967164   .1530774    -0.63   0.528    -.3988848    .2054521
         2001  |   .0750045   .1654516     0.45   0.651    -.2524364    .4024453
         2002  |   .0491619   .1453484     0.34   0.736    -.2374135    .3357373
         2003  |   .1183833    .135278     0.88   0.382    -.1478188    .3845854
         2004  |   .1371986   .1347543     1.02   0.309    -.1279175    .4023147
         2005  |   .1912068   .1373292     1.39   0.165    -.0790673     .461481
         2006  |   .2507063   .1388021     1.81   0.072    -.0224476    .5238601
         2007  |    .243862   .1386874     1.76   0.080    -.0289842    .5167082
         2008  |    .316174   .1406207     2.25   0.025     .0395188    .5928291
         2009  |   .3254625   .1385252     2.35   0.019     .0529469    .5979781
         2010  |   .3354396   .1403342     2.39   0.017      .059352    .6115272
               |
        region |
            2  |    .170893   .1515384     1.13   0.260    -.1266867    .4684728
            3  |   .4748945   .1562516     3.04   0.002     .1680433    .7817456
            4  |   .1823539   .1554732     1.17   0.241    -.1227574    .4874653
            5  |   .1296238   .1499643     0.86   0.388    -.1648614    .4241089
            6  |   .2367772   .1806703     1.31   0.190    -.1179003    .5914547
            7  |   .4735625   .1018699     4.65   0.000     .2735354    .6735896
            8  |   .4164736   .1274527     3.27   0.001     .1662808    .6666665
            9  |   .6200539   .1238256     5.01   0.000     .3768041    .8633036
           10  |   .5313407   .1414662     3.76   0.000     .2534764    .8092051
               |
         _cons |   4.574708   .5000189     9.15   0.000     3.590147    5.559269
--------------------------------------------------------------------------------

. eststo ti_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3368
                                                Largest FMI       =     0.6106
                                                Complete DF       =       1143
DF adjustment:   Small sample                   DF:     min       =     103.57
                                                        avg       =     411.98
                                                        max       =     948.27
Model F test:       Equal FMI                   F(  32, 1087.0)   =     300.45
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .5825146   .0388221    15.00   0.000     .5058255    .6592038
               |
      pctwomen |  -.0303404   .0062525    -4.85   0.000    -.0426327   -.0180481
     lagXwomen |   .0053126   .0012219     4.35   0.000     .0029081     .007717
        fh_neg |  -.1984829    .039235    -5.06   0.000    -.2755335   -.1214322
       log_gdp |  -.3649319   .0440323    -8.29   0.000    -.4515212   -.2783427
pct_protestant |  -.0024491   .0012444    -1.97   0.049    -.0048924   -5.82e-06
  trade_impexp |  -.0006665   .0006714    -0.99   0.321    -.0019845    .0006515
         wecon |  -.0168448    .049729    -0.34   0.735    -.1146355    .0809458
               |
          year |
         1995  |  -.2614512    .183491    -1.42   0.157    -.6253383    .1024359
         1996  |  -.1078676   .1724107    -0.63   0.533    -.4491933    .2334581
         1997  |   .0051638   .1576594     0.03   0.974    -.3061975    .3165251
         1998  |  -.0183751    .153578    -0.12   0.905    -.3214456    .2846953
         1999  |  -.0967164   .1530774    -0.63   0.528    -.3988848    .2054521
         2001  |   .0750045   .1654516     0.45   0.651    -.2524364    .4024453
         2002  |   .0491619   .1453484     0.34   0.736    -.2374135    .3357373
         2003  |   .1183833    .135278     0.88   0.382    -.1478188    .3845854
         2004  |   .1371986   .1347543     1.02   0.309    -.1279175    .4023147
         2005  |   .1912068   .1373292     1.39   0.165    -.0790673     .461481
         2006  |   .2507063   .1388021     1.81   0.072    -.0224476    .5238601
         2007  |    .243862   .1386874     1.76   0.080    -.0289842    .5167082
         2008  |    .316174   .1406207     2.25   0.025     .0395188    .5928291
         2009  |   .3254625   .1385252     2.35   0.019     .0529469    .5979781
         2010  |   .3354396   .1403342     2.39   0.017      .059352    .6115272
               |
        region |
            2  |    .170893   .1515384     1.13   0.260    -.1266867    .4684728
            3  |   .4748945   .1562516     3.04   0.002     .1680433    .7817456
            4  |   .1823539   .1554732     1.17   0.241    -.1227574    .4874653
            5  |   .1296238   .1499643     0.86   0.388    -.1648614    .4241089
            6  |   .2367772   .1806703     1.31   0.190    -.1179003    .5914547
            7  |   .4735625   .1018699     4.65   0.000     .2735354    .6735896
            8  |   .4164736   .1274527     3.27   0.001     .1662808    .6666665
            9  |   .6200539   .1238256     5.01   0.000     .3768041    .8633036
           10  |   .5313407   .1414662     3.76   0.000     .2534764    .8092051
               |
         _cons |   4.574708   .5000189     9.15   0.000     3.590147    5.559269
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  58800

. 
. mibeta cpi_ti l.cpi_ti pctwomen lagXwomen fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3368
                                                Largest FMI       =     0.6097
                                                Complete DF       =       1143
DF adjustment:   Small sample                   DF:     min       =     103.57
                                                        avg       =     411.98
                                                        max       =     948.27
Model F test:       Equal FMI                   F(  32, 1087.0)   =     300.45
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .5825146   .0388221    15.00   0.000     .5058255    .6592038
               |
      pctwomen |  -.0303404   .0062525    -4.85   0.000    -.0426327   -.0180481
     lagXwomen |   .0053126   .0012219     4.35   0.000     .0029081     .007717
        fh_neg |  -.1984829    .039235    -5.06   0.000    -.2755335   -.1214322
       log_gdp |  -.3649319   .0440323    -8.29   0.000    -.4515212   -.2783427
pct_protestant |  -.0024491   .0012444    -1.97   0.049    -.0048924   -5.82e-06
  trade_impexp |  -.0006665   .0006714    -0.99   0.321    -.0019845    .0006515
         wecon |  -.0168448    .049729    -0.34   0.735    -.1146355    .0809458
               |
          year |
         1995  |  -.2614512    .183491    -1.42   0.157    -.6253383    .1024359
         1996  |  -.1078676   .1724107    -0.63   0.533    -.4491933    .2334581
         1997  |   .0051638   .1576594     0.03   0.974    -.3061975    .3165251
         1998  |  -.0183751    .153578    -0.12   0.905    -.3214456    .2846953
         1999  |  -.0967164   .1530774    -0.63   0.528    -.3988848    .2054521
         2001  |   .0750045   .1654516     0.45   0.651    -.2524364    .4024453
         2002  |   .0491619   .1453484     0.34   0.736    -.2374135    .3357373
         2003  |   .1183833    .135278     0.88   0.382    -.1478188    .3845854
         2004  |   .1371986   .1347543     1.02   0.309    -.1279175    .4023147
         2005  |   .1912068   .1373292     1.39   0.165    -.0790673     .461481
         2006  |   .2507063   .1388021     1.81   0.072    -.0224476    .5238601
         2007  |    .243862   .1386874     1.76   0.080    -.0289842    .5167082
         2008  |    .316174   .1406207     2.25   0.025     .0395188    .5928291
         2009  |   .3254625   .1385252     2.35   0.019     .0529469    .5979781
         2010  |   .3354396   .1403342     2.39   0.017      .059352    .6115272
               |
        region |
            2  |    .170893   .1515384     1.13   0.260    -.1266867    .4684728
            3  |   .4748945   .1562516     3.04   0.002     .1680433    .7817456
            4  |   .1823539   .1554732     1.17   0.241    -.1227574    .4874653
            5  |   .1296238   .1499643     0.86   0.388    -.1648614    .4241089
            6  |   .2367772   .1806703     1.31   0.190    -.1179003    .5914547
            7  |   .4735625   .1018699     4.65   0.000     .2735354    .6735896
            8  |   .4164736   .1274527     3.27   0.001     .1662808    .6666665
            9  |   .6200539   .1238256     5.01   0.000     .3768041    .8633036
           10  |   .5313407   .1414662     3.76   0.000     .2534764    .8092051
               |
         _cons |   4.574708   .5000189     9.15   0.000     3.590147    5.559269
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
      cpi_ti |
         L1. |   .5856549        .5   .5703886   .5855108   .6023057      .633
             |
    pctwomen |  -.1288412     -.156  -.1372887  -.1286871  -.1209814    -.0943
   lagXwomen |   .1123152     .0793   .1024769   .1126022   .1229731       .14
      fh_neg |  -.0851255     -.103  -.0886844  -.0849576  -.0819328    -.0709
     log_gdp |  -.2267593     -.263  -.2366096   -.228712  -.2181468     -.198
pct_protes~t |  -.0273435    -.0387  -.0304182  -.0270286  -.0237039    -.0182
trade_impexp |  -.0106505    -.0197  -.0129723  -.0105666  -.0086407   -.00379
       wecon |   -.004694    -.0196  -.0104636  -.0053534   .0003404     .0128
             |
        year |
       1995  |  -.0258044    -.0563  -.0313337  -.0261357  -.0189482     .0141
       1996  |  -.0106306    -.0353  -.0177058  -.0103885  -.0015331     .0233
       1997  |   .0005051    -.0275  -.0055218   .0014115   .0071205     .0322
       1998  |  -.0017996    -.0302  -.0080419   .0001261   .0036348       .02
       1999  |  -.0097896    -.0312  -.0158124   -.010213  -.0014271     .0112
       2001  |   .0077569    -.0224  -.0016609   .0104763   .0153581     .0303
       2002  |   .0050874    -.0154   .0000295   .0050222   .0111082     .0237
       2003  |   .0122374   -.00209   .0072741   .0110817   .0192979     .0271
       2004  |   .0141744   -.00609   .0097168   .0151826   .0190857     .0296
       2005  |   .0197522     .0035   .0129581    .019883   .0265977     .0361
       2006  |    .025732    .00958   .0200447    .026028   .0319354       .04
       2007  |   .0248764     .0074   .0192026   .0238637   .0309265     .0413
       2008  |   .0322484      .015    .026456    .032637   .0382994     .0475
       2009  |    .032986     .0162   .0273047   .0329882   .0385282     .0478
       2010  |   .0339958     .0144    .028643    .034896   .0404836     .0497
             |
      region |
          2  |   .0136149    .00358   .0110144   .0130438   .0172905     .0245
          3  |   .0394574     .0269   .0357092   .0403153    .043358     .0492
          4  |   .0143744    .00752   .0114538   .0149605   .0168082     .0211
          5  |   .0107725   .000792   .0065187   .0109219   .0144263      .021
          6  |   .0161795    .00779   .0130214   .0159205    .019794     .0244
          7  |   .0853139     .0684   .0807946   .0855863   .0904372     .0984
          8  |   .0440278     .0328   .0414173    .042744   .0478144     .0524
          9  |   .0939578     .0712   .0894239   .0950869   .0995838      .109
         10  |    .095946     .0729    .088144   .0962325   .1033101      .115
-------------+----------------------------------------------------------------
    R-square |   .9188719      .904   .9171537   .9192327   .9223874      .927
Adj R-square |   .9166006      .901   .9148343   .9169715   .9202145      .924
------------------------------------------------------------------------------

. 
. ****
. * ICRG Corruption Measure
. ****
. 
. * load in the data
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename icrg_corr icrg_corr_o

. gen icrg_corr = 6 - icrg_corr_o
(292 missing values generated)

. 
. * scatterplot: ICRG Measure
. twoway (scatter icrg_corr pctwomen if l.icrg_corr<=3 & l.icrg_corr!=. & exclude_new!=1) (lfit icrg_corr pctwomen if l.icrg_corr<=3 & l.icr
> g_corr!=. & exclude_new!=1), title("Low Prior Corruption") xtitle("% Women in Lower House") ytitle("ICRG Corruption Score") legend(label(1
>  "ICRG Score") label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6) scheme(s2mono)

. *graph export icrg-lag-lo.emf, replace
. twoway (scatter icrg_corr pctwomen if l.icrg_corr>3 & l.icrg_corr!=. & exclude_new!=1) (lfit icrg_corr pctwomen if l.icrg_corr>3 & l.icrg_
> corr!=. & exclude_new!=1), title("High Prior Corruption") xtitle("% Women in Lower House") ytitle("ICRG Corruption Score") legend(label(1 
> "ICRG Score") label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6) scheme(s2mono)

. *graph export icrg-lag-hi.emf, replace
. 
. qui reg icrg_corr l.icrg_corr pctwomen if exclude_new!=1

. unique country if e(sample)
Number of unique values of country is  76
Number of records is  1417

. 
. gen lagdum = .
(2,002 missing values generated)

. replace lagdum = 0 if l.icrg_corr<=3 & l.icrg_corr!=.
(968 real changes made)

. replace lagdum = 1 if l.icrg_corr>3 & l.icrg_corr!=.
(641 real changes made)

. gen womXlagdum = pctwomen*lagdum
(400 missing values generated)

. reg icrg_corr pctwomen lagdum womXlagdum if(exclude_new!=1)

      Source |       SS           df       MS      Number of obs   =     1,417
-------------+----------------------------------   F(3, 1413)      =    817.91
       Model |  1566.81825         3  522.272751   Prob > F        =    0.0000
    Residual |   902.26642     1,413  .638546653   R-squared       =    0.6346
-------------+----------------------------------   Adj R-squared   =    0.6338
       Total |  2469.08467     1,416  1.74370387   Root MSE        =    .79909

------------------------------------------------------------------------------
   icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    pctwomen |  -.0522533   .0024565   -21.27   0.000    -.0570721   -.0474345
      lagdum |   1.191757    .084727    14.07   0.000     1.025552    1.357961
  womXlagdum |   .0460839   .0047747     9.65   0.000     .0367175    .0554502
       _cons |   2.772624   .0491756    56.38   0.000     2.676159    2.869089
------------------------------------------------------------------------------

. 
. xtunitroot fisher icrg_corr if exclude_new!=1, trend pperron lags(1)
(292 missing values generated)

Fisher-type unit-root test for icrg_corr
Based on Phillips-Perron tests
----------------------------------------
Ho: All panels contain unit roots           Number of panels       =     76
Ha: At least one panel is stationary        Avg. number of periods =  19.66

AR parameter:    Panel-specific             Asymptotics: T -> Infinity
Panel means:     Included
Time trend:      Included
Newey-West lags: 1 lag
------------------------------------------------------------------------------
                                  Statistic      p-value
------------------------------------------------------------------------------
 Inverse chi-squared(152)  P       161.5863       0.2821
 Inverse normal            Z         0.1005       0.5400
 Inverse logit t(369)      L*       -0.3970       0.3458
 Modified inv. chi-squared Pm        0.5498       0.2912
------------------------------------------------------------------------------
 P statistic requires number of panels to be finite.
 Other statistics are suitable for finite or infinite number of panels.
------------------------------------------------------------------------------

. xtunitroot fisher icrg_corr if exclude_new!=1, trend dfuller lags(1)
(292 missing values generated)

Fisher-type unit-root test for icrg_corr
Based on augmented Dickey-Fuller tests
----------------------------------------
Ho: All panels contain unit roots           Number of panels       =     76
Ha: At least one panel is stationary        Avg. number of periods =  19.66

AR parameter: Panel-specific                Asymptotics: T -> Infinity
Panel means:  Included
Time trend:   Included
Drift term:   Not included                  ADF regressions: 1 lag
------------------------------------------------------------------------------
                                  Statistic      p-value
------------------------------------------------------------------------------
 Inverse chi-squared(152)  P       289.6761       0.0000
 Inverse normal            Z        -4.3654       0.0000
 Inverse logit t(369)      L*       -6.0710       0.0000
 Modified inv. chi-squared Pm        7.8963       0.0000
------------------------------------------------------------------------------
 P statistic requires number of panels to be finite.
 Other statistics are suitable for finite or infinite number of panels.
------------------------------------------------------------------------------

. 
. 
. * check proportion of cases with l.icrg_corr > 5
. qui reg icrg_corr l.icrg_corr  if exclude_new!=1

. gen highicrg = .
(2,002 missing values generated)

. replace highicrg = 0 if l.icrg_corr<5 & l.icrg_corr!=. & e(sample)
(1,388 real changes made)

. replace highicrg = 1 if l.icrg_corr>=5 & l.icrg_corr!=. & e(sample)
(29 real changes made)

. sum highicrg

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    highicrg |      1,417    .0204658    .1416372          0          1

. 
. 
. ice icrg_corr pctwomen fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1, seed(123456) m(50) saving(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,474       73.63       73.63
          1 |         20        1.00       74.63
          . |        508       25.37      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
  icrg_corr |         | [No missing data in estimation sample]
   pctwomen |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | icrg_corr pctwomen fh_neg log_gdp pct_protestant
            |         | trade_impexp
trade_imp~p | regress | icrg_corr pctwomen fh_neg log_gdp pct_protestant wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. qui mi xeq: sort countryid year; by countryid: gen lagXwomen = l.icrg_corr * pctwomen

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg icrg_corr l.icrg_corr pctwomen lagXwomen fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exc
> lude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0005
                                                Largest FMI       =     0.0100
                                                Complete DF       =       1380
DF adjustment:   Small sample                   DF:     min       =   1,360.58
                                                        avg       =   1,376.98
                                                        max       =   1,378.00
Model F test:       Equal FMI                   F(  36, 1378.0)   =     519.16
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8075835   .0176493    45.76   0.000      .772961     .842206
               |
      pctwomen |  -.0113654   .0022406    -5.07   0.000    -.0157608     -.00697
     lagXwomen |   .0032442   .0007814     4.15   0.000     .0017113    .0047771
        fh_neg |  -.0481483    .015913    -3.03   0.003    -.0793647   -.0169319
       log_gdp |  -.0458236    .014255    -3.21   0.001    -.0737875   -.0178598
pct_protestant |  -.0000131   .0005205    -0.03   0.980    -.0010342     .001008
  trade_impexp |    .000265   .0002891     0.92   0.360    -.0003022    .0008321
         wecon |   .0545841   .0198641     2.75   0.006     .0156166    .0935516
               |
          year |
         1991  |   -.222253   .0630527    -3.52   0.000    -.3459427   -.0985634
         1992  |  -.3328513   .0615194    -5.41   0.000    -.4535332   -.2121695
         1993  |  -.2612518   .0607653    -4.30   0.000    -.3804542   -.1420493
         1994  |  -.1460553   .0599354    -2.44   0.015    -.2636298   -.0284807
         1995  |  -.0819388   .0589408    -1.39   0.165    -.1975622    .0336847
         1996  |  -.1095985   .0587925    -1.86   0.063    -.2249309    .0057339
         1997  |    .001144   .0584508     0.02   0.984    -.1135182    .1158062
         1998  |  -.0176533   .0583181    -0.30   0.762    -.1320552    .0967486
         1999  |   .0183743   .0581866     0.32   0.752    -.0957697    .1325182
         2001  |   .0797225   .0568962     1.40   0.161      -.03189    .1913349
         2002  |   .4782662   .0570135     8.39   0.000     .3664235    .5901089
         2003  |   .0108415   .0579258     0.19   0.852    -.1027908    .1244737
         2004  |   .0740512   .0580458     1.28   0.202    -.0398165     .187919
         2005  |  -.0166111   .0583539    -0.28   0.776    -.1310833     .097861
         2006  |     .10896   .0587856     1.85   0.064     -.006359     .224279
         2007  |   .0557244   .0595112     0.94   0.349    -.0610179    .1724668
         2008  |   .0492057   .0600459     0.82   0.413    -.0685856     .166997
         2009  |    .018412   .0596856     0.31   0.758    -.0986726    .1354965
         2010  |  -.0185406   .0599931    -0.31   0.757    -.1362283     .099147
               |
        region |
            2  |  -.0176314   .0627092    -0.28   0.779    -.1406472    .1053844
            3  |    .021098   .0642109     0.33   0.743    -.1048636    .1470596
            4  |   .0270062   .0688031     0.39   0.695    -.1079639    .1619764
            5  |   .0042756   .0625656     0.07   0.946    -.1184586    .1270097
            6  |   .0227795   .0768025     0.30   0.767    -.1278829    .1734419
            7  |   .0591052   .0428767     1.38   0.168    -.0250054    .1432158
            8  |   .0091009   .0548154     0.17   0.868    -.0984299    .1166317
            9  |   .0891347   .0490329     1.82   0.069    -.0070526     .185322
           10  |  -.0016549   .0575297    -0.03   0.977    -.1145102    .1112003
               |
         _cons |   .7583019    .149551     5.07   0.000     .4649295    1.051674
--------------------------------------------------------------------------------

. eststo icrg_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0005
                                                Largest FMI       =     0.0100
                                                Complete DF       =       1380
DF adjustment:   Small sample                   DF:     min       =   1,360.58
                                                        avg       =   1,376.98
                                                        max       =   1,378.00
Model F test:       Equal FMI                   F(  36, 1378.0)   =     519.16
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8075835   .0176493    45.76   0.000      .772961     .842206
               |
      pctwomen |  -.0113654   .0022406    -5.07   0.000    -.0157608     -.00697
     lagXwomen |   .0032442   .0007814     4.15   0.000     .0017113    .0047771
        fh_neg |  -.0481483    .015913    -3.03   0.003    -.0793647   -.0169319
       log_gdp |  -.0458236    .014255    -3.21   0.001    -.0737875   -.0178598
pct_protestant |  -.0000131   .0005205    -0.03   0.980    -.0010342     .001008
  trade_impexp |    .000265   .0002891     0.92   0.360    -.0003022    .0008321
         wecon |   .0545841   .0198641     2.75   0.006     .0156166    .0935516
               |
          year |
         1991  |   -.222253   .0630527    -3.52   0.000    -.3459427   -.0985634
         1992  |  -.3328513   .0615194    -5.41   0.000    -.4535332   -.2121695
         1993  |  -.2612518   .0607653    -4.30   0.000    -.3804542   -.1420493
         1994  |  -.1460553   .0599354    -2.44   0.015    -.2636298   -.0284807
         1995  |  -.0819388   .0589408    -1.39   0.165    -.1975622    .0336847
         1996  |  -.1095985   .0587925    -1.86   0.063    -.2249309    .0057339
         1997  |    .001144   .0584508     0.02   0.984    -.1135182    .1158062
         1998  |  -.0176533   .0583181    -0.30   0.762    -.1320552    .0967486
         1999  |   .0183743   .0581866     0.32   0.752    -.0957697    .1325182
         2001  |   .0797225   .0568962     1.40   0.161      -.03189    .1913349
         2002  |   .4782662   .0570135     8.39   0.000     .3664235    .5901089
         2003  |   .0108415   .0579258     0.19   0.852    -.1027908    .1244737
         2004  |   .0740512   .0580458     1.28   0.202    -.0398165     .187919
         2005  |  -.0166111   .0583539    -0.28   0.776    -.1310833     .097861
         2006  |     .10896   .0587856     1.85   0.064     -.006359     .224279
         2007  |   .0557244   .0595112     0.94   0.349    -.0610179    .1724668
         2008  |   .0492057   .0600459     0.82   0.413    -.0685856     .166997
         2009  |    .018412   .0596856     0.31   0.758    -.0986726    .1354965
         2010  |  -.0185406   .0599931    -0.31   0.757    -.1362283     .099147
               |
        region |
            2  |  -.0176314   .0627092    -0.28   0.779    -.1406472    .1053844
            3  |    .021098   .0642109     0.33   0.743    -.1048636    .1470596
            4  |   .0270062   .0688031     0.39   0.695    -.1079639    .1619764
            5  |   .0042756   .0625656     0.07   0.946    -.1184586    .1270097
            6  |   .0227795   .0768025     0.30   0.767    -.1278829    .1734419
            7  |   .0591052   .0428767     1.38   0.168    -.0250054    .1432158
            8  |   .0091009   .0548154     0.17   0.868    -.0984299    .1166317
            9  |   .0891347   .0490329     1.82   0.069    -.0070526     .185322
           10  |  -.0016549   .0575297    -0.03   0.977    -.1145102    .1112003
               |
         _cons |   .7583019    .149551     5.07   0.000     .4649295    1.051674
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  70850

. 
. mibeta icrg_corr l.icrg_corr pctwomen lagXwomen fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0005
                                                Largest FMI       =     0.0099
                                                Complete DF       =       1380
DF adjustment:   Small sample                   DF:     min       =   1,360.58
                                                        avg       =   1,376.98
                                                        max       =   1,378.00
Model F test:       Equal FMI                   F(  36, 1378.0)   =     519.16
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8075835   .0176493    45.76   0.000      .772961     .842206
               |
      pctwomen |  -.0113654   .0022406    -5.07   0.000    -.0157608     -.00697
     lagXwomen |   .0032442   .0007814     4.15   0.000     .0017113    .0047771
        fh_neg |  -.0481483    .015913    -3.03   0.003    -.0793647   -.0169319
       log_gdp |  -.0458236    .014255    -3.21   0.001    -.0737875   -.0178598
pct_protestant |  -.0000131   .0005205    -0.03   0.980    -.0010342     .001008
  trade_impexp |    .000265   .0002891     0.92   0.360    -.0003022    .0008321
         wecon |   .0545841   .0198641     2.75   0.006     .0156166    .0935516
               |
          year |
         1991  |   -.222253   .0630527    -3.52   0.000    -.3459427   -.0985634
         1992  |  -.3328513   .0615194    -5.41   0.000    -.4535332   -.2121695
         1993  |  -.2612518   .0607653    -4.30   0.000    -.3804542   -.1420493
         1994  |  -.1460553   .0599354    -2.44   0.015    -.2636298   -.0284807
         1995  |  -.0819388   .0589408    -1.39   0.165    -.1975622    .0336847
         1996  |  -.1095985   .0587925    -1.86   0.063    -.2249309    .0057339
         1997  |    .001144   .0584508     0.02   0.984    -.1135182    .1158062
         1998  |  -.0176533   .0583181    -0.30   0.762    -.1320552    .0967486
         1999  |   .0183743   .0581866     0.32   0.752    -.0957697    .1325182
         2001  |   .0797225   .0568962     1.40   0.161      -.03189    .1913349
         2002  |   .4782662   .0570135     8.39   0.000     .3664235    .5901089
         2003  |   .0108415   .0579258     0.19   0.852    -.1027908    .1244737
         2004  |   .0740512   .0580458     1.28   0.202    -.0398165     .187919
         2005  |  -.0166111   .0583539    -0.28   0.776    -.1310833     .097861
         2006  |     .10896   .0587856     1.85   0.064     -.006359     .224279
         2007  |   .0557244   .0595112     0.94   0.349    -.0610179    .1724668
         2008  |   .0492057   .0600459     0.82   0.413    -.0685856     .166997
         2009  |    .018412   .0596856     0.31   0.758    -.0986726    .1354965
         2010  |  -.0185406   .0599931    -0.31   0.757    -.1362283     .099147
               |
        region |
            2  |  -.0176314   .0627092    -0.28   0.779    -.1406472    .1053844
            3  |    .021098   .0642109     0.33   0.743    -.1048636    .1470596
            4  |   .0270062   .0688031     0.39   0.695    -.1079639    .1619764
            5  |   .0042756   .0625656     0.07   0.946    -.1184586    .1270097
            6  |   .0227795   .0768025     0.30   0.767    -.1278829    .1734419
            7  |   .0591052   .0428767     1.38   0.168    -.0250054    .1432158
            8  |   .0091009   .0548154     0.17   0.868    -.0984299    .1166317
            9  |   .0891347   .0490329     1.82   0.069    -.0070526     .185322
           10  |  -.0016549   .0575297    -0.03   0.977    -.1145102    .1112003
               |
         _cons |   .7583019    .149551     5.07   0.000     .4649295    1.051674
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
   icrg_corr |
         L1. |   .8149031      .815   .8148191   .8148908   .8149959      .815
             |
    pctwomen |  -.0873604    -.0879  -.0875415   -.087346  -.0871728    -.0868
   lagXwomen |   .0716325     .0713   .0714663   .0716405   .0717696      .072
      fh_neg |  -.0377611    -.0382   -.037895  -.0377567  -.0376502    -.0373
     log_gdp |  -.0512702     -.052  -.0514196    -.05127   -.051106    -.0504
pct_protes~t |   -.000265  -.000543  -.0003167  -.0002473  -.0001922  -.000056
trade_impexp |   .0076888    .00577   .0072733   .0076866   .0081601    .00926
       wecon |   .0271036     .0251   .0264469   .0270867   .0277802      .029
             |
        year |
       1991  |  -.0325211    -.0327  -.0325813   -.032528  -.0324838    -.0323
       1992  |  -.0507765     -.051  -.0508271  -.0507458  -.0507173    -.0505
       1993  |   -.040793    -.0409  -.0408158  -.0407926   -.040763    -.0407
       1994  |  -.0233165    -.0235  -.0233924  -.0232845  -.0232489    -.0232
       1995  |    -.01336    -.0136  -.0134709  -.0133536  -.0132573    -.0131
       1996  |  -.0178699     -.018  -.0179396  -.0178798  -.0178098    -.0176
       1997  |   .0001878   .000037   .0001104    .000207   .0002374    .00041
       1998  |   -.002898   -.00317  -.0029871  -.0028753  -.0028205   -.00261
       1999  |   .0030164    .00291   .0029965   .0030203   .0030455     .0031
       2001  |   .0136066     .0136   .0135914    .013605   .0136175     .0137
       2002  |   .0816278     .0816   .0816103   .0816294   .0816466     .0817
       2003  |   .0018504    .00179   .0018313   .0018526   .0018701    .00192
       2004  |   .0126386     .0126   .0126264   .0126364   .0126524     .0127
       2005  |  -.0028351   -.00289  -.0028462  -.0028362  -.0028274   -.00277
       2006  |   .0184808     .0183   .0184317   .0184819    .018525     .0187
       2007  |   .0093918    .00927   .0093334    .009391    .009436    .00957
       2008  |   .0082931    .00806   .0082328   .0082864   .0083449    .00854
       2009  |   .0030832    .00299   .0030497   .0030831   .0031155    .00319
       2010  |  -.0031048   -.00324  -.0031478  -.0031212  -.0030543   -.00296
             |
      region |
          2  |  -.0025799   -.00283  -.0026518  -.0025686  -.0025023   -.00225
          3  |   .0032185    .00291   .0031416   .0032226   .0033118    .00344
          4  |    .003882    .00319   .0036974   .0039099   .0040652    .00464
          5  |   .0006522   .000341    .000584   .0006653    .000721   .000913
          6  |   .0028582    .00261   .0028008   .0028689   .0029306    .00309
          7  |   .0193288     .0189   .0191856   .0193677   .0194453     .0198
          8  |   .0017493    .00116   .0016217   .0017614   .0019015    .00251
          9  |   .0234114     .0225   .0232363   .0234456   .0236301     .0242
         10  |  -.0005453   -.00158  -.0007659  -.0004689  -.0002956   .000565
-------------+----------------------------------------------------------------
    R-square |   .9312739      .931   .9312576   .9312727   .9312955      .931
Adj R-square |    .929481      .929   .9294643   .9294799   .9295032       .93
------------------------------------------------------------------------------

. 
. 
. 
. ****
. * World Bank Governance Indicator Measure
. ****
. 
. * load in the data
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename wb_corr wb_corr_o

. gen wb_corr = 2.6 - wb_corr_o
(816 missing values generated)

. 
. * scatterplot: WBGI Measure
. twoway (scatter wb_corr pctwomen if l.wb_corr<=2 & l.wb_corr!=. & exclude_new!=1) (lfit wb_corr pctwomen if l.wb_corr<=2 & l.wb_corr!=. & 
> exclude_new!=1), scheme(s2mono)

. twoway (scatter wb_corr pctwomen if l.wb_corr>2  & l.wb_corr!=. & exclude_new!=1) (lfit wb_corr pctwomen if l.wb_corr>2 & l.wb_corr!=. & e
> xclude_new!=1), scheme(s2mono)

. 
. * model: WBGI Measure, with lag
. 
. ice wb_corr pctwomen fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1995, seed(123456) m(50) saving(wb_imputed, repl
> ace)

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        877       43.81       43.81
          1 |        298       14.89       58.69
          2 |          5        0.25       58.94
          . |        822       41.06      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | wb_corr pctwomen fh_neg log_gdp pct_protestant
            |         | trade_impexp
trade_imp~p | regress | wb_corr pctwomen fh_neg log_gdp pct_protestant wecon
    wb_corr | regress | pctwomen fh_neg log_gdp pct_protestant trade_impexp
            |         | wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. qui mi xeq: sort countryid year; by countryid: gen lagXwomen = l.wb_corr * pctwomen

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg wb_corr l.wb_corr pctwomen lagXwomen fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude
> _new!=1 

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.3484
                                                Largest FMI       =     0.6231
                                                Complete DF       =       1077
DF adjustment:   Small sample                   DF:     min       =      98.28
                                                        avg       =     539.59
                                                        max       =     815.65
Model F test:       Equal FMI                   F(  31, 1023.4)   =     166.69
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .2615741   .0591931     4.42   0.000     .1441695    .3789787
               |
      pctwomen |  -.0179591   .0037529    -4.79   0.000    -.0253408   -.0105774
     lagXwomen |   .0060553   .0016616     3.64   0.000      .002784    .0093265
        fh_neg |  -.2005859   .0272532    -7.36   0.000    -.2542646   -.1469072
       log_gdp |  -.2877887   .0274592   -10.48   0.000    -.3418987   -.2336788
pct_protestant |  -.0020753    .000755    -2.75   0.006    -.0035592   -.0005914
  trade_impexp |   .0000587   .0004008     0.15   0.884    -.0007284    .0008458
         wecon |  -.0225908   .0266771    -0.85   0.397    -.0749698    .0297882
               |
          year |
         1996  |  -.0825318   .0693332    -1.19   0.234    -.2186421    .0535786
         1997  |   .3022707   .0957348     3.16   0.002     .1128253     .491716
         1998  |  -.0546982   .0705947    -0.77   0.439     -.193332    .0839357
         1999  |   .2745566   .1030806     2.66   0.009     .0700039    .4791094
         2001  |     .27865   .0944737     2.95   0.004     .0916266    .4656734
         2002  |   .0904218   .0685011     1.32   0.187    -.0440872    .2249308
         2003  |   .1519057   .0666679     2.28   0.023     .0210449    .2827666
         2004  |   .2308381    .066837     3.45   0.001     .0996441     .362032
         2005  |   .2924162   .0672359     4.35   0.000     .1604362    .4243963
         2006  |   .3005551   .0684093     4.39   0.000     .1662668    .4348433
         2007  |   .3644842   .0690817     5.28   0.000      .228871    .5000974
         2008  |   .3774779   .0704563     5.36   0.000     .2391613    .5157944
         2009  |   .3816704   .0692933     5.51   0.000     .2456439     .517697
         2010  |   .3941523   .0702674     5.61   0.000     .2562049    .5320997
               |
        region |
            2  |   .0429688   .0899186     0.48   0.633    -.1336869    .2196244
            3  |   .3468889   .0906415     3.83   0.000     .1688966    .5248813
            4  |   .1231061   .0949787     1.30   0.195    -.0633984    .3096106
            5  |   .2059735   .0862392     2.39   0.017     .0366741     .375273
            6  |   .2857979   .1063561     2.69   0.007     .0769937     .494602
            7  |   .3913025   .0607365     6.44   0.000     .2720167    .5105884
            8  |   .3835377   .0768683     4.99   0.000     .2325873     .534488
            9  |   .4609274   .0735581     6.27   0.000     .3163798     .605475
           10  |   .3420646   .0817164     4.19   0.000     .1815965    .5025327
               |
         _cons |   3.372705   .3139468    10.74   0.000     2.752588    3.992822
--------------------------------------------------------------------------------

. eststo wbgi_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.3484
                                                Largest FMI       =     0.6231
                                                Complete DF       =       1077
DF adjustment:   Small sample                   DF:     min       =      98.28
                                                        avg       =     539.59
                                                        max       =     815.65
Model F test:       Equal FMI                   F(  31, 1023.4)   =     166.69
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .2615741   .0591931     4.42   0.000     .1441695    .3789787
               |
      pctwomen |  -.0179591   .0037529    -4.79   0.000    -.0253408   -.0105774
     lagXwomen |   .0060553   .0016616     3.64   0.000      .002784    .0093265
        fh_neg |  -.2005859   .0272532    -7.36   0.000    -.2542646   -.1469072
       log_gdp |  -.2877887   .0274592   -10.48   0.000    -.3418987   -.2336788
pct_protestant |  -.0020753    .000755    -2.75   0.006    -.0035592   -.0005914
  trade_impexp |   .0000587   .0004008     0.15   0.884    -.0007284    .0008458
         wecon |  -.0225908   .0266771    -0.85   0.397    -.0749698    .0297882
               |
          year |
         1996  |  -.0825318   .0693332    -1.19   0.234    -.2186421    .0535786
         1997  |   .3022707   .0957348     3.16   0.002     .1128253     .491716
         1998  |  -.0546982   .0705947    -0.77   0.439     -.193332    .0839357
         1999  |   .2745566   .1030806     2.66   0.009     .0700039    .4791094
         2001  |     .27865   .0944737     2.95   0.004     .0916266    .4656734
         2002  |   .0904218   .0685011     1.32   0.187    -.0440872    .2249308
         2003  |   .1519057   .0666679     2.28   0.023     .0210449    .2827666
         2004  |   .2308381    .066837     3.45   0.001     .0996441     .362032
         2005  |   .2924162   .0672359     4.35   0.000     .1604362    .4243963
         2006  |   .3005551   .0684093     4.39   0.000     .1662668    .4348433
         2007  |   .3644842   .0690817     5.28   0.000      .228871    .5000974
         2008  |   .3774779   .0704563     5.36   0.000     .2391613    .5157944
         2009  |   .3816704   .0692933     5.51   0.000     .2456439     .517697
         2010  |   .3941523   .0702674     5.61   0.000     .2562049    .5320997
               |
        region |
            2  |   .0429688   .0899186     0.48   0.633    -.1336869    .2196244
            3  |   .3468889   .0906415     3.83   0.000     .1688966    .5248813
            4  |   .1231061   .0949787     1.30   0.195    -.0633984    .3096106
            5  |   .2059735   .0862392     2.39   0.017     .0366741     .375273
            6  |   .2857979   .1063561     2.69   0.007     .0769937     .494602
            7  |   .3913025   .0607365     6.44   0.000     .2720167    .5105884
            8  |   .3835377   .0768683     4.99   0.000     .2325873     .534488
            9  |   .4609274   .0735581     6.27   0.000     .3163798     .605475
           10  |   .3420646   .0817164     4.19   0.000     .1815965    .5025327
               |
         _cons |   3.372705   .3139468    10.74   0.000     2.752588    3.992822
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  55450

. 
. mibeta wb_corr l.wb_corr pctwomen lagXwomen fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1 

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.3484
                                                Largest FMI       =     0.6221
                                                Complete DF       =       1077
DF adjustment:   Small sample                   DF:     min       =      98.28
                                                        avg       =     539.59
                                                        max       =     815.65
Model F test:       Equal FMI                   F(  31, 1023.4)   =     166.69
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .2615741   .0591931     4.42   0.000     .1441695    .3789787
               |
      pctwomen |  -.0179591   .0037529    -4.79   0.000    -.0253408   -.0105774
     lagXwomen |   .0060553   .0016616     3.64   0.000      .002784    .0093265
        fh_neg |  -.2005859   .0272532    -7.36   0.000    -.2542646   -.1469072
       log_gdp |  -.2877887   .0274592   -10.48   0.000    -.3418987   -.2336788
pct_protestant |  -.0020753    .000755    -2.75   0.006    -.0035592   -.0005914
  trade_impexp |   .0000587   .0004008     0.15   0.884    -.0007284    .0008458
         wecon |  -.0225908   .0266771    -0.85   0.397    -.0749698    .0297882
               |
          year |
         1996  |  -.0825318   .0693332    -1.19   0.234    -.2186421    .0535786
         1997  |   .3022707   .0957348     3.16   0.002     .1128253     .491716
         1998  |  -.0546982   .0705947    -0.77   0.439     -.193332    .0839357
         1999  |   .2745566   .1030806     2.66   0.009     .0700039    .4791094
         2001  |     .27865   .0944737     2.95   0.004     .0916266    .4656734
         2002  |   .0904218   .0685011     1.32   0.187    -.0440872    .2249308
         2003  |   .1519057   .0666679     2.28   0.023     .0210449    .2827666
         2004  |   .2308381    .066837     3.45   0.001     .0996441     .362032
         2005  |   .2924162   .0672359     4.35   0.000     .1604362    .4243963
         2006  |   .3005551   .0684093     4.39   0.000     .1662668    .4348433
         2007  |   .3644842   .0690817     5.28   0.000      .228871    .5000974
         2008  |   .3774779   .0704563     5.36   0.000     .2391613    .5157944
         2009  |   .3816704   .0692933     5.51   0.000     .2456439     .517697
         2010  |   .3941523   .0702674     5.61   0.000     .2562049    .5320997
               |
        region |
            2  |   .0429688   .0899186     0.48   0.633    -.1336869    .2196244
            3  |   .3468889   .0906415     3.83   0.000     .1688966    .5248813
            4  |   .1231061   .0949787     1.30   0.195    -.0633984    .3096106
            5  |   .2059735   .0862392     2.39   0.017     .0366741     .375273
            6  |   .2857979   .1063561     2.69   0.007     .0769937     .494602
            7  |   .3913025   .0607365     6.44   0.000     .2720167    .5105884
            8  |   .3835377   .0768683     4.99   0.000     .2325873     .534488
            9  |   .4609274   .0735581     6.27   0.000     .3163798     .605475
           10  |   .3420646   .0817164     4.19   0.000     .1815965    .5025327
               |
         _cons |   3.372705   .3139468    10.74   0.000     2.752588    3.992822
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
     wb_corr |
         L1. |   .2628391      .133   .2327651   .2536471   .3002438      .352
             |
    pctwomen |   -.172977      -.22  -.1847179  -.1733405  -.1599569     -.128
   lagXwomen |   .1259285      .086   .1128992   .1269177   .1378734      .176
      fh_neg |  -.1927502     -.229  -.2009023  -.1918221  -.1818909     -.164
     log_gdp |  -.4036088     -.478  -.4163002  -.4069032  -.3861115     -.362
pct_protes~t |  -.0524228    -.0711  -.0591349  -.0523262   -.044755     -.037
trade_impexp |   .0021106    -.0113    -.00221   .0018422   .0066067      .015
       wecon |  -.0142326    -.0288  -.0180271   -.014627  -.0116128    .00387
             |
        year |
       1996  |  -.0189453    -.0315  -.0221661  -.0192772  -.0155655   -.00633
       1997  |   .0698682     .0346   .0602477   .0715754   .0792616     .0965
       1998  |  -.0126378    -.0273  -.0156293  -.0130673  -.0084059  -.000833
       1999  |   .0655525     .0247    .050017   .0685672   .0799778      .107
       2001  |   .0669446     .0277   .0580348   .0666201   .0806654     .0971
       2002  |   .0217144    .00809   .0175832   .0238861   .0256801     .0391
       2003  |   .0364886     .0275   .0324464    .036675   .0400985     .0479
       2004  |   .0554469     .0464   .0511165   .0559997   .0590036      .067
       2005  |   .0702363     .0609   .0662166   .0709403   .0738597     .0822
       2006  |   .0717483      .062   .0678658   .0724684   .0755386     .0844
       2007  |   .0864697     .0756   .0820441   .0867486   .0906273      .098
       2008  |    .089551     .0792   .0859093   .0894195   .0937117      .102
       2009  |    .089976     .0791   .0856593   .0900003   .0939476      .102
       2010  |   .0929188     .0811   .0888174   .0927472   .0978201      .104
             |
      region |
          2  |   .0077034    -.0116   .0035864   .0082592   .0138533     .0211
          3  |   .0650637     .0501   .0615634   .0654445   .0676996     .0868
          4  |    .021825    .00404   .0181658   .0218161   .0257271      .037
          5  |   .0386347     .0233   .0346791   .0382654   .0428232     .0496
          6  |   .0440772     .0277   .0397347   .0450714   .0471672     .0556
          7  |   .1592464      .136   .1511869   .1584137   .1680054      .178
          8  |   .0915558      .074   .0873773   .0916504   .0949604      .107
          9  |   .1601069      .137   .1518935   .1600125   .1689859      .188
         10  |   .1395616      .109    .130181   .1414258   .1497419      .165
-------------+----------------------------------------------------------------
    R-square |   .8668784       .84   .8627125   .8678222   .8710406      .879
Adj R-square |   .8630466      .835   .8587609   .8640176   .8673286      .876
------------------------------------------------------------------------------

. 
. esttab ti_est icrg_est wbgi_est using lag.rtf, replace order(L.cpi_ti L.icrg_corr L.wb_corr pctwomen lagXwomen fh_neg log_gdp pct_protesta
> nt trade_impexp wecon) keep(L.cpi_ti L.icrg_corr L.wb_corr pctwomen lagXwomen fh_neg log_gdp pct_protestant trade_impexp wecon) mtitles("T
> I CPI" "ICRG" "WBGI") coeflabels(L.cpi_ti "lag TI CPI" L.icrg_corr "lag ICRG" L.wb_corr "lag WBGI" pctwomen "% women in lower house" lagXw
> omen "% women * lag DV" fh_neg "FH Freedom" log_gdp "log GDP per capita" pct_protestant "% protestant" trade_impexp "trade imbalance (% of
>  GDP)" wecon "women's economic rights" _cons "constant") noabbrev wrap gaps varwidth(25) align(r)
(output written to lag.rtf)

. 
. 
. 
. 
. 
. 
. 
. *********************************
. * Press freedom: use press3_inverse so that higher values are more freedom 
. *********************************
. 
. ****
. * Transparency International Measure
. ****
. 
. clear all

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename cpi_ti cpi_ti_o

. gen cpi_ti = 10 - cpi_ti_o
(739 missing values generated)

. 
. * create interaction variable
. gen womenXpress3=pctwomen*press3_inverse
(241 missing values generated)

. 
. * scatterplots: Transparency International Corruption Measure
. twoway (scatter cpi_ti pctwomen if press3_inverse < -30 & press3_inverse!=. & exclude_new!=1) (lfit cpi_ti pctwomen if press3_inverse < -3
> 0 & press3_inverse!=. & exclude_new!=1), title("Low Press Freedom") xtitle("% Women in Lower House") ytitle("TI Corruption Perception Inde
> x") legend(label(1 "TI CPI") label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6 7 8 9 10) scheme(s2mono)

. graph export ti-press-lo.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-press-lo.emf written in Enhanced Metafile format)

. twoway (scatter cpi_ti pctwomen if press3_inverse >= -30 & press3_inverse!=. & exclude_new!=1) (lfit cpi_ti pctwomen if press3_inverse >= 
> -30 & press3_inverse!=. & exclude_new!=1), title("High Press Freedom") xtitle("% Women in Lower House") ytitle("TI Corruption Perception I
> ndex") legend(label(1 "TI CPI") label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6 7 8 9 10) scheme(s2mono)

. graph export ti-press-hi.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-press-hi.emf written in Enhanced Metafile format)

. 
. 
. gen pressdum = .
(2,002 missing values generated)

. replace pressdum = 0 if press3_inverse<=-30 & press3_inverse!=.
(1,000 real changes made)

. replace pressdum = 1 if press3_inverse>-30 & press3_inverse!=.
(769 real changes made)

. gen womXpressdum = pctwomen*pressdum
(241 missing values generated)

. reg cpi_ti pctwomen pressdum womXpressdum if(exclude_new!=1)

      Source |       SS           df       MS      Number of obs   =     1,029
-------------+----------------------------------   F(3, 1025)      =    543.95
       Model |  3462.16949         3   1154.0565   Prob > F        =    0.0000
    Residual |  2174.67642     1,025  2.12163553   R-squared       =    0.6142
-------------+----------------------------------   Adj R-squared   =    0.6131
       Total |  5636.84591     1,028  5.48331314   Root MSE        =    1.4566

------------------------------------------------------------------------------
      cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    pctwomen |  -.0032978   .0085475    -0.39   0.700    -.0200705    .0134748
    pressdum |  -.8633346    .191899    -4.50   0.000    -1.239894   -.4867748
womXpressdum |  -.1104129   .0102706   -10.75   0.000    -.1305668   -.0902591
       _cons |    6.61619   .1370249    48.28   0.000     6.347309    6.885072
------------------------------------------------------------------------------

. unique country if e(sample)
Number of unique values of country is  76
Number of records is  1029

. tab year if e(sample)

       Year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1995 |         35        3.40        3.40
       1996 |         41        3.98        7.39
       1997 |         42        4.08       11.47
       1998 |         58        5.64       17.10
       1999 |         67        6.51       23.62
       2000 |         58        5.64       29.25
       2001 |         66        6.41       35.67
       2002 |         70        6.80       42.47
       2003 |         74        7.19       49.66
       2004 |         75        7.29       56.95
       2005 |         76        7.39       64.33
       2006 |         74        7.19       71.53
       2007 |         74        7.19       78.72
       2008 |         74        7.19       85.91
       2009 |         73        7.09       93.00
       2010 |         72        7.00      100.00
------------+-----------------------------------
      Total |      1,029      100.00

. 
. 
. * generate multiple imputation data sets
. ice cpi_ti pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1994, seed(123456) m(
> 50) saving(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,016       50.75       50.75
          1 |        228       11.39       62.14
          2 |          5        0.25       62.39
          . |        753       37.61      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
press3_in~e |         | [No missing data in estimation sample]
womenXpre~3 |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | cpi_ti pctwomen press3_inverse womenXpress3 fh_neg
            |         | log_gdp pct_protestant trade_impexp
trade_imp~p | regress | cpi_ti pctwomen press3_inverse womenXpress3 fh_neg
            |         | log_gdp pct_protestant wecon
     cpi_ti | regress | pctwomen press3_inverse womenXpress3 fh_neg log_gdp
            |         | pct_protestant trade_impexp wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg cpi_ti l.cpi_ti pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon i.year i.r
> egion if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3670
                                                Largest FMI       =     0.5944
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     108.96
                                                        avg       =     433.04
                                                        max       =     868.17
Model F test:       Equal FMI                   F(  33, 1081.1)   =     297.42
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6811203   .0323311    21.07   0.000     .6170407    .7451998
               |
      pctwomen |  -.0263327   .0059084    -4.46   0.000    -.0379347   -.0147307
press3_inverse |   .0095119   .0042808     2.22   0.027     .0010984    .0179254
  womenXpress3 |  -.0008022   .0001912    -4.20   0.000    -.0011777   -.0004268
        fh_neg |  -.1814418   .0616254    -2.94   0.003    -.3026884   -.0601952
       log_gdp |  -.3239139   .0452923    -7.15   0.000    -.4131167   -.2347111
pct_protestant |   -.002836   .0011922    -2.38   0.018    -.0051771   -.0004949
  trade_impexp |  -.0004086   .0006976    -0.59   0.558    -.0017795    .0009624
         wecon |  -.0383369   .0462431    -0.83   0.407    -.1291931    .0525193
               |
          year |
         1995  |  -.1774976   .1642047    -1.08   0.282    -.5023671    .1473719
         1996  |  -.0526511   .1687882    -0.31   0.756     -.386894    .2815917
         1997  |    .040876   .1517588     0.27   0.788     -.258762     .340514
         1998  |  -.0025534    .164486    -0.02   0.988    -.3280872    .3229803
         1999  |  -.0560197   .1387574    -0.40   0.687    -.3294058    .2173664
         2001  |   .1215039   .1615562     0.75   0.453    -.1982836    .4412913
         2002  |   .1056465   .1277434     0.83   0.409    -.1455811    .3568742
         2003  |    .145338   .1296392     1.12   0.263    -.1096801    .4003561
         2004  |   .1644019    .129337     1.27   0.205    -.0899705    .4187743
         2005  |   .2053502   .1280445     1.60   0.110    -.0463823    .4570827
         2006  |    .240132   .1313889     1.83   0.068    -.0182394    .4985034
         2007  |   .2520684   .1310111     1.92   0.055    -.0054867    .5096234
         2008  |   .2976509   .1319075     2.26   0.025     .0383784    .5569235
         2009  |   .3168169    .130902     2.42   0.016     .0594959    .5741379
         2010  |    .327644   .1337855     2.45   0.015     .0645962    .5906917
               |
        region |
            2  |   .1454559   .1476773     0.98   0.325    -.1444888    .4354006
            3  |   .4033952   .1502075     2.69   0.007     .1084415    .6983489
            4  |   .1663991   .1525094     1.09   0.276    -.1329311    .4657293
            5  |   .0408367   .1408036     0.29   0.772    -.2355438    .3172172
            6  |    .266744   .1747772     1.53   0.127    -.0762988    .6097869
            7  |   .4234858   .0977337     4.33   0.000     .2315778    .6153939
            8  |   .4222507   .1323493     3.19   0.001     .1623483    .6821532
            9  |   .5625251   .1225874     4.59   0.000     .3216061    .8034441
           10  |   .4505017    .133202     3.38   0.001     .1889558    .7120476
               |
         _cons |    4.05999   .5210219     7.79   0.000     3.031805    5.088174
--------------------------------------------------------------------------------

. 
. eststo ti_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3670
                                                Largest FMI       =     0.5944
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     108.96
                                                        avg       =     433.04
                                                        max       =     868.17
Model F test:       Equal FMI                   F(  33, 1081.1)   =     297.42
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6811203   .0323311    21.07   0.000     .6170407    .7451998
               |
      pctwomen |  -.0263327   .0059084    -4.46   0.000    -.0379347   -.0147307
press3_inverse |   .0095119   .0042808     2.22   0.027     .0010984    .0179254
  womenXpress3 |  -.0008022   .0001912    -4.20   0.000    -.0011777   -.0004268
        fh_neg |  -.1814418   .0616254    -2.94   0.003    -.3026884   -.0601952
       log_gdp |  -.3239139   .0452923    -7.15   0.000    -.4131167   -.2347111
pct_protestant |   -.002836   .0011922    -2.38   0.018    -.0051771   -.0004949
  trade_impexp |  -.0004086   .0006976    -0.59   0.558    -.0017795    .0009624
         wecon |  -.0383369   .0462431    -0.83   0.407    -.1291931    .0525193
               |
          year |
         1995  |  -.1774976   .1642047    -1.08   0.282    -.5023671    .1473719
         1996  |  -.0526511   .1687882    -0.31   0.756     -.386894    .2815917
         1997  |    .040876   .1517588     0.27   0.788     -.258762     .340514
         1998  |  -.0025534    .164486    -0.02   0.988    -.3280872    .3229803
         1999  |  -.0560197   .1387574    -0.40   0.687    -.3294058    .2173664
         2001  |   .1215039   .1615562     0.75   0.453    -.1982836    .4412913
         2002  |   .1056465   .1277434     0.83   0.409    -.1455811    .3568742
         2003  |    .145338   .1296392     1.12   0.263    -.1096801    .4003561
         2004  |   .1644019    .129337     1.27   0.205    -.0899705    .4187743
         2005  |   .2053502   .1280445     1.60   0.110    -.0463823    .4570827
         2006  |    .240132   .1313889     1.83   0.068    -.0182394    .4985034
         2007  |   .2520684   .1310111     1.92   0.055    -.0054867    .5096234
         2008  |   .2976509   .1319075     2.26   0.025     .0383784    .5569235
         2009  |   .3168169    .130902     2.42   0.016     .0594959    .5741379
         2010  |    .327644   .1337855     2.45   0.015     .0645962    .5906917
               |
        region |
            2  |   .1454559   .1476773     0.98   0.325    -.1444888    .4354006
            3  |   .4033952   .1502075     2.69   0.007     .1084415    .6983489
            4  |   .1663991   .1525094     1.09   0.276    -.1329311    .4657293
            5  |   .0408367   .1408036     0.29   0.772    -.2355438    .3172172
            6  |    .266744   .1747772     1.53   0.127    -.0762988    .6097869
            7  |   .4234858   .0977337     4.33   0.000     .2315778    .6153939
            8  |   .4222507   .1323493     3.19   0.001     .1623483    .6821532
            9  |   .5625251   .1225874     4.59   0.000     .3216061    .8034441
           10  |   .4505017    .133202     3.38   0.001     .1889558    .7120476
               |
         _cons |    4.05999   .5210219     7.79   0.000     3.031805    5.088174
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  58800

. 
. mibeta cpi_ti l.cpi_ti pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3670
                                                Largest FMI       =     0.5934
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     108.96
                                                        avg       =     433.04
                                                        max       =     868.17
Model F test:       Equal FMI                   F(  33, 1081.1)   =     297.42
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6811203   .0323311    21.07   0.000     .6170407    .7451998
               |
      pctwomen |  -.0263327   .0059084    -4.46   0.000    -.0379347   -.0147307
press3_inverse |   .0095119   .0042808     2.22   0.027     .0010984    .0179254
  womenXpress3 |  -.0008022   .0001912    -4.20   0.000    -.0011777   -.0004268
        fh_neg |  -.1814418   .0616254    -2.94   0.003    -.3026884   -.0601952
       log_gdp |  -.3239139   .0452923    -7.15   0.000    -.4131167   -.2347111
pct_protestant |   -.002836   .0011922    -2.38   0.018    -.0051771   -.0004949
  trade_impexp |  -.0004086   .0006976    -0.59   0.558    -.0017795    .0009624
         wecon |  -.0383369   .0462431    -0.83   0.407    -.1291931    .0525193
               |
          year |
         1995  |  -.1774976   .1642047    -1.08   0.282    -.5023671    .1473719
         1996  |  -.0526511   .1687882    -0.31   0.756     -.386894    .2815917
         1997  |    .040876   .1517588     0.27   0.788     -.258762     .340514
         1998  |  -.0025534    .164486    -0.02   0.988    -.3280872    .3229803
         1999  |  -.0560197   .1387574    -0.40   0.687    -.3294058    .2173664
         2001  |   .1215039   .1615562     0.75   0.453    -.1982836    .4412913
         2002  |   .1056465   .1277434     0.83   0.409    -.1455811    .3568742
         2003  |    .145338   .1296392     1.12   0.263    -.1096801    .4003561
         2004  |   .1644019    .129337     1.27   0.205    -.0899705    .4187743
         2005  |   .2053502   .1280445     1.60   0.110    -.0463823    .4570827
         2006  |    .240132   .1313889     1.83   0.068    -.0182394    .4985034
         2007  |   .2520684   .1310111     1.92   0.055    -.0054867    .5096234
         2008  |   .2976509   .1319075     2.26   0.025     .0383784    .5569235
         2009  |   .3168169    .130902     2.42   0.016     .0594959    .5741379
         2010  |    .327644   .1337855     2.45   0.015     .0645962    .5906917
               |
        region |
            2  |   .1454559   .1476773     0.98   0.325    -.1444888    .4354006
            3  |   .4033952   .1502075     2.69   0.007     .1084415    .6983489
            4  |   .1663991   .1525094     1.09   0.276    -.1329311    .4657293
            5  |   .0408367   .1408036     0.29   0.772    -.2355438    .3172172
            6  |    .266744   .1747772     1.53   0.127    -.0762988    .6097869
            7  |   .4234858   .0977337     4.33   0.000     .2315778    .6153939
            8  |   .4222507   .1323493     3.19   0.001     .1623483    .6821532
            9  |   .5625251   .1225874     4.59   0.000     .3216061    .8034441
           10  |   .4505017    .133202     3.38   0.001     .1889558    .7120476
               |
         _cons |    4.05999   .5210219     7.79   0.000     3.031805    5.088174
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
      cpi_ti |
         L1. |   .6829068      .625   .6683133    .683241   .7018943      .738
             |
    pctwomen |  -.1130022     -.136  -.1204595  -.1130696  -.1058166    -.0948
press3_inv~e |   .0659497     .0363   .0556657   .0652376   .0745455      .101
womenXpress3 |  -.0978627     -.116  -.1046498  -.0986992  -.0902041    -.0774
      fh_neg |  -.0786462     -.121  -.0870849  -.0761688  -.0699758    -.0476
     log_gdp |  -.2034289     -.241  -.2112549    -.20324  -.1906803     -.173
pct_protes~t |  -.0320128    -.0451  -.0355153  -.0323308  -.0281178    -.0177
trade_impexp |  -.0066038    -.0198  -.0086954  -.0059012  -.0031174     .0032
       wecon |  -.0107965    -.0301  -.0147488  -.0114859  -.0064536   .000701
             |
        year |
       1995  |  -.0177278    -.0507  -.0256641  -.0152058  -.0110437    .00213
       1996  |  -.0052381    -.0261  -.0153511  -.0056155   .0036661     .0258
       1997  |   .0041026    -.0244  -.0026246   .0028043   .0103724     .0244
       1998  |  -.0002359    -.0214  -.0088581  -.0005965   .0068069     .0243
       1999  |  -.0057363    -.0274  -.0098382  -.0070652  -.0009619     .0172
       2001  |   .0126942    -.0115   .0027338   .0123298   .0229093     .0377
       2002  |   .0110269  -.000149   .0062156   .0100936   .0157607     .0266
       2003  |   .0151739   -.00188   .0101134   .0154129   .0197959     .0354
       2004  |   .0171608    .00535   .0114191   .0166329   .0226679     .0331
       2005  |   .0214333     .0076   .0165534   .0204401    .026069     .0387
       2006  |   .0249093    .00863   .0207047   .0246985   .0299041     .0426
       2007  |   .0259817     .0145   .0206901   .0250828   .0310303     .0437
       2008  |   .0306806     .0178   .0256313   .0297644   .0359399      .048
       2009  |   .0324496     .0191   .0276628   .0318954   .0375966     .0504
       2010  |   .0335554     .0192    .028786   .0327601   .0386223     .0534
             |
      region |
          2  |   .0117105    .00261   .0089958   .0118864    .013546     .0241
          3  |   .0338723     .0219   .0306589    .034348   .0365785     .0468
          4  |   .0132576    .00559   .0102366   .0127357   .0170089     .0201
          5  |   .0034266   -.00655   .0011642   .0028763   .0065709     .0108
          6  |   .0184189    .00879   .0161686   .0187871   .0204788     .0254
          7  |   .0771033     .0643   .0734315   .0772033    .080576      .093
          8  |   .0451242     .0318   .0405832   .0446995   .0488006     .0601
          9  |   .0861491     .0666   .0792452    .086113    .089906      .113
         10  |   .0822164     .0634   .0760554   .0824842   .0880039      .104
-------------+----------------------------------------------------------------
    R-square |   .9220678      .905   .9182182   .9213936   .9262802      .933
Adj R-square |   .9198158      .902    .915855   .9191221   .9241499      .931
------------------------------------------------------------------------------

. 
. 
. ****
. * ICRG Corruption Measure
. ****
. 
. * load in the data
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. * recode the DV
. rename icrg_corr icrg_corr_o

. gen icrg_corr = 6 - icrg_corr_o
(292 missing values generated)

. 
. * create interaction variable
. gen womenXpress3=pctwomen*press3_inverse
(241 missing values generated)

. 
. * scatterplots: ICRG Corruption Measure
. twoway (scatter icrg_corr pctwomen if press3_inverse < -30 & press3_inverse!=. & exclude_new!=1) (lfit icrg_corr pctwomen if press3_invers
> e < -30 & press3_inverse!=. & exclude_new!=1), title("Low Press Freedom") xtitle("% Women in Lower House") ytitle("ICRG Corruption Score")
>  legend(label(1 "ICRG Score") label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6) scheme(s2mono)

. 
. *graph export icrg-press-lo.emf, replace
. 
. twoway (scatter icrg_corr pctwomen if press3_inverse >= -30 & press3_inverse!=. & exclude_new!=1) (lfit icrg_corr pctwomen if press3_inver
> se >= -30 & press3_inverse!=. & exclude_new!=1), title("High Press Freedom") xtitle("% Women in Lower House") ytitle("ICRG Corruption Scor
> e") legend(label(1 "ICRG Score") label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6) scheme(s2mono)

. 
. *graph export icrg-press-hi.emf, replace
. 
. gen pressdum = .
(2,002 missing values generated)

. replace pressdum = 0 if press3_inverse<=-30 & press3_inverse!=.
(1,000 real changes made)

. replace pressdum = 1 if press3_inverse>-30 & press3_inverse!=.
(769 real changes made)

. gen womXpressdum = pctwomen*pressdum
(241 missing values generated)

. reg icrg_corr pctwomen pressdum womXpressdum if(exclude_new!=1)

      Source |       SS           df       MS      Number of obs   =     1,315
-------------+----------------------------------   F(3, 1311)      =    252.32
       Model |  805.620769         3  268.540256   Prob > F        =    0.0000
    Residual |  1395.25695     1,311  1.06426922   R-squared       =    0.3660
-------------+----------------------------------   Adj R-squared   =    0.3646
       Total |  2200.87772     1,314  1.67494499   Root MSE        =    1.0316

------------------------------------------------------------------------------
   icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    pctwomen |   .0119496   .0053751     2.22   0.026     .0014049    .0224943
    pressdum |   .0233787   .1139465     0.21   0.837    -.2001588    .2469161
womXpressdum |    -.06982   .0064707   -10.79   0.000     -.082514    -.057126
       _cons |   3.170852   .0802549    39.51   0.000      3.01341    3.328294
------------------------------------------------------------------------------

. unique country if e(sample)
Number of unique values of country is  76
Number of records is  1315

. 
. 
. * generate multiple imputation data sets
. ice icrg_corr pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1, passive(womenXpress3: pct
> women*press3_inverse) seed(123456) m(50) saving(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,297       64.79       64.79
          1 |         18        0.90       65.68
          2 |        177        8.84       74.53
          3 |          2        0.10       74.63
          . |        508       25.37      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
  icrg_corr |         | [No missing data in estimation sample]
   pctwomen |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | icrg_corr pctwomen press3_inverse womenXpress3 fh_neg
            |         | log_gdp pct_protestant trade_impexp
trade_imp~p | regress | icrg_corr pctwomen press3_inverse womenXpress3 fh_neg
            |         | log_gdp pct_protestant wecon
press3_in~e | regress | icrg_corr pctwomen fh_neg log_gdp pct_protestant
            |         | trade_impexp wecon
womenXpre~3 |         | [Passively imputed from pctwomen*press3_inverse]
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg icrg_corr l.icrg_corr pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon i.ye
> ar i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0056
                                                Largest FMI       =     0.1331
                                                Complete DF       =       1379
DF adjustment:   Small sample                   DF:     min       =     837.63
                                                        avg       =   1,349.66
                                                        max       =   1,376.97
Model F test:       Equal FMI                   F(  37, 1376.9)   =     498.43
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8563296   .0124839    68.59   0.000     .8318401    .8808192
               |
      pctwomen |  -.0093554   .0024906    -3.76   0.000    -.0142414   -.0044694
press3_inverse |   .0039731   .0018047     2.20   0.028     .0004308    .0075154
  womenXpress3 |  -.0002071   .0000809    -2.56   0.011    -.0003658   -.0000484
        fh_neg |  -.0672928   .0239006    -2.82   0.005    -.1141887   -.0203969
       log_gdp |   -.038779   .0142361    -2.72   0.007    -.0667059   -.0108522
pct_protestant |  -.0005641   .0005172    -1.09   0.276    -.0015788    .0004505
  trade_impexp |   .0002878   .0002918     0.99   0.324    -.0002846    .0008602
         wecon |   .0544554   .0200516     2.72   0.007     .0151198    .0937909
               |
          year |
         1991  |  -.2135602   .0633066    -3.37   0.001    -.3377482   -.0893723
         1992  |  -.3217756   .0617648    -5.21   0.000    -.4429393    -.200612
         1993  |  -.2564578   .0610543    -4.20   0.000    -.3762274   -.1366882
         1994  |  -.1374187   .0601126    -2.29   0.022    -.2553409   -.0194965
         1995  |  -.0831444   .0592335    -1.40   0.161    -.1993421    .0330532
         1996  |  -.1083062   .0590199    -1.84   0.067    -.2240848    .0074725
         1997  |   .0021855   .0586785     0.04   0.970    -.1129235    .1172945
         1998  |  -.0153969   .0585326    -0.26   0.793    -.1302197    .0994259
         1999  |   .0194366    .058404     0.33   0.739    -.0951339    .1340071
         2001  |   .0778078   .0571446     1.36   0.174    -.0342921    .1899078
         2002  |   .4793415   .0572385     8.37   0.000     .3670574    .5916255
         2003  |   .0162511   .0582471     0.28   0.780    -.0980117    .1305139
         2004  |   .0815027   .0584624     1.39   0.164    -.0331823    .1961878
         2005  |  -.0078608   .0588911    -0.13   0.894    -.1233869    .1076653
         2006  |   .1156599   .0594045     1.95   0.052    -.0008734    .2321931
         2007  |   .0632403   .0600952     1.05   0.293    -.0546478    .1811284
         2008  |   .0555742   .0607579     0.91   0.361    -.0636142    .1747625
         2009  |   .0241566   .0603551     0.40   0.689    -.0942415    .1425547
         2010  |  -.0152488   .0607581    -0.25   0.802    -.1344375    .1039398
               |
        region |
            2  |  -.0330655   .0645636    -0.51   0.609    -.1597197    .0935887
            3  |  -.0173963   .0639466    -0.27   0.786    -.1428398    .1080472
            4  |  -.0108315   .0682215    -0.16   0.874     -.144661     .122998
            5  |  -.0542628   .0623792    -0.87   0.385    -.1766319    .0681062
            6  |   -.003006   .0775326    -0.04   0.969    -.1551011     .149089
            7  |   .0286319   .0420119     0.68   0.496    -.0537824    .1110462
            8  |   -.012377   .0560026    -0.22   0.825    -.1222373    .0974834
            9  |   .0702136   .0490926     1.43   0.153     -.026091    .1665182
           10  |  -.0488296   .0566874    -0.86   0.389    -.1600328    .0623735
               |
         _cons |   .6902988   .1488749     4.64   0.000     .3982523    .9823454
--------------------------------------------------------------------------------

. 
. eststo icrg_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0056
                                                Largest FMI       =     0.1331
                                                Complete DF       =       1379
DF adjustment:   Small sample                   DF:     min       =     837.63
                                                        avg       =   1,349.66
                                                        max       =   1,376.97
Model F test:       Equal FMI                   F(  37, 1376.9)   =     498.43
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8563296   .0124839    68.59   0.000     .8318401    .8808192
               |
      pctwomen |  -.0093554   .0024906    -3.76   0.000    -.0142414   -.0044694
press3_inverse |   .0039731   .0018047     2.20   0.028     .0004308    .0075154
  womenXpress3 |  -.0002071   .0000809    -2.56   0.011    -.0003658   -.0000484
        fh_neg |  -.0672928   .0239006    -2.82   0.005    -.1141887   -.0203969
       log_gdp |   -.038779   .0142361    -2.72   0.007    -.0667059   -.0108522
pct_protestant |  -.0005641   .0005172    -1.09   0.276    -.0015788    .0004505
  trade_impexp |   .0002878   .0002918     0.99   0.324    -.0002846    .0008602
         wecon |   .0544554   .0200516     2.72   0.007     .0151198    .0937909
               |
          year |
         1991  |  -.2135602   .0633066    -3.37   0.001    -.3377482   -.0893723
         1992  |  -.3217756   .0617648    -5.21   0.000    -.4429393    -.200612
         1993  |  -.2564578   .0610543    -4.20   0.000    -.3762274   -.1366882
         1994  |  -.1374187   .0601126    -2.29   0.022    -.2553409   -.0194965
         1995  |  -.0831444   .0592335    -1.40   0.161    -.1993421    .0330532
         1996  |  -.1083062   .0590199    -1.84   0.067    -.2240848    .0074725
         1997  |   .0021855   .0586785     0.04   0.970    -.1129235    .1172945
         1998  |  -.0153969   .0585326    -0.26   0.793    -.1302197    .0994259
         1999  |   .0194366    .058404     0.33   0.739    -.0951339    .1340071
         2001  |   .0778078   .0571446     1.36   0.174    -.0342921    .1899078
         2002  |   .4793415   .0572385     8.37   0.000     .3670574    .5916255
         2003  |   .0162511   .0582471     0.28   0.780    -.0980117    .1305139
         2004  |   .0815027   .0584624     1.39   0.164    -.0331823    .1961878
         2005  |  -.0078608   .0588911    -0.13   0.894    -.1233869    .1076653
         2006  |   .1156599   .0594045     1.95   0.052    -.0008734    .2321931
         2007  |   .0632403   .0600952     1.05   0.293    -.0546478    .1811284
         2008  |   .0555742   .0607579     0.91   0.361    -.0636142    .1747625
         2009  |   .0241566   .0603551     0.40   0.689    -.0942415    .1425547
         2010  |  -.0152488   .0607581    -0.25   0.802    -.1344375    .1039398
               |
        region |
            2  |  -.0330655   .0645636    -0.51   0.609    -.1597197    .0935887
            3  |  -.0173963   .0639466    -0.27   0.786    -.1428398    .1080472
            4  |  -.0108315   .0682215    -0.16   0.874     -.144661     .122998
            5  |  -.0542628   .0623792    -0.87   0.385    -.1766319    .0681062
            6  |   -.003006   .0775326    -0.04   0.969    -.1551011     .149089
            7  |   .0286319   .0420119     0.68   0.496    -.0537824    .1110462
            8  |   -.012377   .0560026    -0.22   0.825    -.1222373    .0974834
            9  |   .0702136   .0490926     1.43   0.153     -.026091    .1665182
           10  |  -.0488296   .0566874    -0.86   0.389    -.1600328    .0623735
               |
         _cons |   .6902988   .1488749     4.64   0.000     .3982523    .9823454
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  70850

. 
. mibeta icrg_corr l.icrg_corr pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_ne
> w!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0056
                                                Largest FMI       =     0.1329
                                                Complete DF       =       1379
DF adjustment:   Small sample                   DF:     min       =     837.63
                                                        avg       =   1,349.66
                                                        max       =   1,376.97
Model F test:       Equal FMI                   F(  37, 1376.9)   =     498.43
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8563296   .0124839    68.59   0.000     .8318401    .8808192
               |
      pctwomen |  -.0093554   .0024906    -3.76   0.000    -.0142414   -.0044694
press3_inverse |   .0039731   .0018047     2.20   0.028     .0004308    .0075154
  womenXpress3 |  -.0002071   .0000809    -2.56   0.011    -.0003658   -.0000484
        fh_neg |  -.0672928   .0239006    -2.82   0.005    -.1141887   -.0203969
       log_gdp |   -.038779   .0142361    -2.72   0.007    -.0667059   -.0108522
pct_protestant |  -.0005641   .0005172    -1.09   0.276    -.0015788    .0004505
  trade_impexp |   .0002878   .0002918     0.99   0.324    -.0002846    .0008602
         wecon |   .0544554   .0200516     2.72   0.007     .0151198    .0937909
               |
          year |
         1991  |  -.2135602   .0633066    -3.37   0.001    -.3377482   -.0893723
         1992  |  -.3217756   .0617648    -5.21   0.000    -.4429393    -.200612
         1993  |  -.2564578   .0610543    -4.20   0.000    -.3762274   -.1366882
         1994  |  -.1374187   .0601126    -2.29   0.022    -.2553409   -.0194965
         1995  |  -.0831444   .0592335    -1.40   0.161    -.1993421    .0330532
         1996  |  -.1083062   .0590199    -1.84   0.067    -.2240848    .0074725
         1997  |   .0021855   .0586785     0.04   0.970    -.1129235    .1172945
         1998  |  -.0153969   .0585326    -0.26   0.793    -.1302197    .0994259
         1999  |   .0194366    .058404     0.33   0.739    -.0951339    .1340071
         2001  |   .0778078   .0571446     1.36   0.174    -.0342921    .1899078
         2002  |   .4793415   .0572385     8.37   0.000     .3670574    .5916255
         2003  |   .0162511   .0582471     0.28   0.780    -.0980117    .1305139
         2004  |   .0815027   .0584624     1.39   0.164    -.0331823    .1961878
         2005  |  -.0078608   .0588911    -0.13   0.894    -.1233869    .1076653
         2006  |   .1156599   .0594045     1.95   0.052    -.0008734    .2321931
         2007  |   .0632403   .0600952     1.05   0.293    -.0546478    .1811284
         2008  |   .0555742   .0607579     0.91   0.361    -.0636142    .1747625
         2009  |   .0241566   .0603551     0.40   0.689    -.0942415    .1425547
         2010  |  -.0152488   .0607581    -0.25   0.802    -.1344375    .1039398
               |
        region |
            2  |  -.0330655   .0645636    -0.51   0.609    -.1597197    .0935887
            3  |  -.0173963   .0639466    -0.27   0.786    -.1428398    .1080472
            4  |  -.0108315   .0682215    -0.16   0.874     -.144661     .122998
            5  |  -.0542628   .0623792    -0.87   0.385    -.1766319    .0681062
            6  |   -.003006   .0775326    -0.04   0.969    -.1551011     .149089
            7  |   .0286319   .0420119     0.68   0.496    -.0537824    .1110462
            8  |   -.012377   .0560026    -0.22   0.825    -.1222373    .0974834
            9  |   .0702136   .0490926     1.43   0.153     -.026091    .1665182
           10  |  -.0488296   .0566874    -0.86   0.389    -.1600328    .0623735
               |
         _cons |   .6902988   .1488749     4.64   0.000     .3982523    .9823454
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
   icrg_corr |
         L1. |   .8640911      .863   .8638164   .8641216    .864302      .865
             |
    pctwomen |  -.0719109    -.0786  -.0741421  -.0718576  -.0701359     -.065
press3_inv~e |   .0491879     .0319   .0437403   .0500655    .053615     .0693
womenXpress3 |  -.0444512    -.0511  -.0461928  -.0440753  -.0429371    -.0379
      fh_neg |  -.0527755    -.0652   -.055309  -.0531836  -.0492654    -.0416
     log_gdp |  -.0433882    -.0444  -.0438991  -.0433681  -.0429849    -.0423
pct_protes~t |  -.0114433    -.0128  -.0117895  -.0113759  -.0110589    -.0105
trade_impexp |   .0083532    .00684   .0077759   .0083424   .0089111    .00967
       wecon |    .027039     .0247   .0263293   .0272716   .0275344     .0295
             |
        year |
       1991  |  -.0312491    -.0319  -.0314603  -.0312841  -.0310149    -.0303
       1992  |  -.0490869    -.0501  -.0493897  -.0490678   -.048775    -.0482
       1993  |  -.0400445    -.0407  -.0401781  -.0400637  -.0399108    -.0396
       1994  |  -.0219378    -.0224   -.022009  -.0219328  -.0218728    -.0217
       1995  |  -.0135566    -.0143   -.013733  -.0135311  -.0134111    -.0132
       1996  |  -.0176591    -.0179  -.0177327  -.0176706  -.0175907    -.0174
       1997  |   .0003588   .000106   .0003073   .0003595   .0004147   .000574
       1998  |  -.0025276   -.00278  -.0025918  -.0025244  -.0024743   -.00225
       1999  |   .0031908    .00311   .0031527   .0031866   .0032343    .00327
       2001  |   .0132798      .013   .0132052   .0132782   .0133523     .0135
       2002  |   .0818113     .0816   .0817422   .0818193   .0818684      .082
       2003  |   .0027736    .00231   .0026001   .0028048   .0029029    .00328
       2004  |   .0139104     .0132   .0136661   .0139327    .014102     .0147
       2005  |  -.0013416   -.00216   -.001615  -.0013394  -.0011243  -.000389
       2006  |   .0196172     .0188   .0193483   .0196136   .0198109     .0207
       2007  |   .0106585    .00991   .0104075   .0106674    .010875     .0117
       2008  |   .0093664    .00856   .0091091   .0093344   .0095978     .0105
       2009  |   .0040452    .00325   .0037763   .0040231   .0042544    .00495
       2010  |  -.0025535   -.00329  -.0028019  -.0025942   -.002324   -.00162
             |
      region |
          2  |  -.0048383    -.0061  -.0052565  -.0048321   -.004353   -.00314
          3  |  -.0026538   -.00331  -.0028425  -.0026309  -.0024623   -.00199
          4  |   -.001557   -.00219   -.001845  -.0015566  -.0012918   -.00102
          5  |  -.0082778   -.00969  -.0086613  -.0081875  -.0078134   -.00726
          6  |  -.0003772  -.000997  -.0006727  -.0003957  -.0001754    .00058
          7  |   .0093633    .00837   .0090773   .0093165   .0096642     .0105
          8  |   -.002379   -.00408  -.0028336  -.0024848  -.0018863  -.000619
          9  |   .0184418     .0173   .0180104   .0183589   .0186258     .0203
         10  |  -.0160887    -.0178  -.0165994  -.0160411  -.0156359    -.0143
-------------+----------------------------------------------------------------
    R-square |   .9308068      .931   .9307576   .9307984   .9308594      .931
Adj R-square |   .9289502      .929   .9288998   .9289417   .9290043      .929
------------------------------------------------------------------------------

. 
. 
. 
. ****
. * World Bank Governance Indicator Measure
. ****
. 
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename wb_corr wb_corr_o

. gen wb_corr = 2.6 - wb_corr_o
(816 missing values generated)

. 
. * create interaction variable
. gen womenXpress3=pctwomen*press3_inverse
(241 missing values generated)

. 
. * scatterplots: World Bank Governance Indicator Corruption Measure
. twoway (scatter wb_corr pctwomen  if press3_inverse < -30 & press3_inverse!=. & exclude_new!=1) (lfit wb_corr pctwomen  if press3_inverse 
> < -30 & exclude_new!=1), scheme(s2mono)

. twoway (scatter wb_corr pctwomen  if press3_inverse >= -30 & press3_inverse!=. & exclude_new!=1) (lfit wb_corr pctwomen  if press3_inverse
>  >= -30 & exclude_new!=1), scheme(s2mono)

. 
. * generate multiple imputation data sets
. ice wb_corr pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1995, seed(123456) m
> (50) saving(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        877       43.81       43.81
          1 |        298       14.89       58.69
          2 |          5        0.25       58.94
          . |        822       41.06      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
press3_in~e |         | [No missing data in estimation sample]
womenXpre~3 |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | wb_corr pctwomen press3_inverse womenXpress3 fh_neg
            |         | log_gdp pct_protestant trade_impexp
trade_imp~p | regress | wb_corr pctwomen press3_inverse womenXpress3 fh_neg
            |         | log_gdp pct_protestant wecon
    wb_corr | regress | pctwomen press3_inverse womenXpress3 fh_neg log_gdp
            |         | pct_protestant trade_impexp wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg wb_corr l.wb_corr pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon i.year i
> .region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.2916
                                                Largest FMI       =     0.6212
                                                Complete DF       =       1076
DF adjustment:   Small sample                   DF:     min       =      98.85
                                                        avg       =     572.59
                                                        max       =     786.61
Model F test:       Equal FMI                   F(  32, 1035.3)   =     178.57
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .3681082   .0399864     9.21   0.000     .2890311    .4471854
               |
      pctwomen |  -.0200219   .0034991    -5.72   0.000    -.0268944   -.0131494
press3_inverse |   .0025958   .0023882     1.09   0.277    -.0020936    .0072851
  womenXpress3 |  -.0005447   .0001102    -4.94   0.000    -.0007611   -.0003284
        fh_neg |  -.1380461   .0357473    -3.86   0.000     -.208323   -.0677692
       log_gdp |   -.272258   .0250223   -10.88   0.000    -.3214847   -.2230313
pct_protestant |  -.0016442   .0006837    -2.40   0.016    -.0029867   -.0003017
  trade_impexp |   .0004162   .0003951     1.05   0.293    -.0003598    .0011922
         wecon |  -.0250915   .0259235    -0.97   0.333    -.0759889    .0258059
               |
          year |
         1996  |  -.0738204   .0709958    -1.04   0.299    -.2133063    .0656655
         1997  |   .2305489   .0913721     2.52   0.013     .0498343    .4112636
         1998  |  -.0504893   .0683451    -0.74   0.460    -.1846947     .083716
         1999  |   .2182043   .0999322     2.18   0.031     .0199132    .4164953
         2001  |   .1832091   .0850914     2.15   0.033     .0151411    .3512772
         2002  |   .1039546   .0684578     1.52   0.129    -.0305279    .2384371
         2003  |   .1261641   .0652947     1.93   0.054    -.0020083    .2543366
         2004  |   .1937412    .065644     2.95   0.003     .0648815    .3226009
         2005  |    .248336   .0661401     3.75   0.000     .1185017    .3781702
         2006  |   .2448769   .0672421     3.64   0.000      .112879    .3768748
         2007  |   .3071655   .0676781     4.54   0.000     .1743114    .4400196
         2008  |   .3110142   .0689474     4.51   0.000     .1756679    .4463606
         2009  |    .317169   .0680106     4.66   0.000     .1836647    .4506733
         2010  |   .3221377   .0688645     4.68   0.000     .1869539    .4573216
               |
        region |
            2  |   .0674586   .0861354     0.78   0.434    -.1016479     .236565
            3  |   .3373376   .0873338     3.86   0.000     .1658613    .5088138
            4  |   .1209224   .0919246     1.32   0.189    -.0595727    .3014176
            5  |   .1829619   .0855916     2.14   0.033     .0148699    .3510538
            6  |   .3036661   .1053146     2.88   0.004     .0968753     .510457
            7  |   .3671963   .0576714     6.37   0.000     .2539428    .4804499
            8  |   .4296462   .0798822     5.38   0.000     .2727376    .5865547
            9  |   .4409378   .0688506     6.40   0.000     .3057179    .5761577
           10  |    .329138   .0799428     4.12   0.000     .1721002    .4861758
               |
         _cons |   3.201694   .2735903    11.70   0.000     2.662817    3.740571
--------------------------------------------------------------------------------

. 
. eststo wbgi_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.2916
                                                Largest FMI       =     0.6212
                                                Complete DF       =       1076
DF adjustment:   Small sample                   DF:     min       =      98.85
                                                        avg       =     572.59
                                                        max       =     786.61
Model F test:       Equal FMI                   F(  32, 1035.3)   =     178.57
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .3681082   .0399864     9.21   0.000     .2890311    .4471854
               |
      pctwomen |  -.0200219   .0034991    -5.72   0.000    -.0268944   -.0131494
press3_inverse |   .0025958   .0023882     1.09   0.277    -.0020936    .0072851
  womenXpress3 |  -.0005447   .0001102    -4.94   0.000    -.0007611   -.0003284
        fh_neg |  -.1380461   .0357473    -3.86   0.000     -.208323   -.0677692
       log_gdp |   -.272258   .0250223   -10.88   0.000    -.3214847   -.2230313
pct_protestant |  -.0016442   .0006837    -2.40   0.016    -.0029867   -.0003017
  trade_impexp |   .0004162   .0003951     1.05   0.293    -.0003598    .0011922
         wecon |  -.0250915   .0259235    -0.97   0.333    -.0759889    .0258059
               |
          year |
         1996  |  -.0738204   .0709958    -1.04   0.299    -.2133063    .0656655
         1997  |   .2305489   .0913721     2.52   0.013     .0498343    .4112636
         1998  |  -.0504893   .0683451    -0.74   0.460    -.1846947     .083716
         1999  |   .2182043   .0999322     2.18   0.031     .0199132    .4164953
         2001  |   .1832091   .0850914     2.15   0.033     .0151411    .3512772
         2002  |   .1039546   .0684578     1.52   0.129    -.0305279    .2384371
         2003  |   .1261641   .0652947     1.93   0.054    -.0020083    .2543366
         2004  |   .1937412    .065644     2.95   0.003     .0648815    .3226009
         2005  |    .248336   .0661401     3.75   0.000     .1185017    .3781702
         2006  |   .2448769   .0672421     3.64   0.000      .112879    .3768748
         2007  |   .3071655   .0676781     4.54   0.000     .1743114    .4400196
         2008  |   .3110142   .0689474     4.51   0.000     .1756679    .4463606
         2009  |    .317169   .0680106     4.66   0.000     .1836647    .4506733
         2010  |   .3221377   .0688645     4.68   0.000     .1869539    .4573216
               |
        region |
            2  |   .0674586   .0861354     0.78   0.434    -.1016479     .236565
            3  |   .3373376   .0873338     3.86   0.000     .1658613    .5088138
            4  |   .1209224   .0919246     1.32   0.189    -.0595727    .3014176
            5  |   .1829619   .0855916     2.14   0.033     .0148699    .3510538
            6  |   .3036661   .1053146     2.88   0.004     .0968753     .510457
            7  |   .3671963   .0576714     6.37   0.000     .2539428    .4804499
            8  |   .4296462   .0798822     5.38   0.000     .2727376    .5865547
            9  |   .4409378   .0688506     6.40   0.000     .3057179    .5761577
           10  |    .329138   .0799428     4.12   0.000     .1721002    .4861758
               |
         _cons |   3.201694   .2735903    11.70   0.000     2.662817    3.740571
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  55450

. 
. mibeta wb_corr l.wb_corr pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.2916
                                                Largest FMI       =     0.6201
                                                Complete DF       =       1076
DF adjustment:   Small sample                   DF:     min       =      98.85
                                                        avg       =     572.59
                                                        max       =     786.61
Model F test:       Equal FMI                   F(  32, 1035.3)   =     178.57
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .3681082   .0399864     9.21   0.000     .2890311    .4471854
               |
      pctwomen |  -.0200219   .0034991    -5.72   0.000    -.0268944   -.0131494
press3_inverse |   .0025958   .0023882     1.09   0.277    -.0020936    .0072851
  womenXpress3 |  -.0005447   .0001102    -4.94   0.000    -.0007611   -.0003284
        fh_neg |  -.1380461   .0357473    -3.86   0.000     -.208323   -.0677692
       log_gdp |   -.272258   .0250223   -10.88   0.000    -.3214847   -.2230313
pct_protestant |  -.0016442   .0006837    -2.40   0.016    -.0029867   -.0003017
  trade_impexp |   .0004162   .0003951     1.05   0.293    -.0003598    .0011922
         wecon |  -.0250915   .0259235    -0.97   0.333    -.0759889    .0258059
               |
          year |
         1996  |  -.0738204   .0709958    -1.04   0.299    -.2133063    .0656655
         1997  |   .2305489   .0913721     2.52   0.013     .0498343    .4112636
         1998  |  -.0504893   .0683451    -0.74   0.460    -.1846947     .083716
         1999  |   .2182043   .0999322     2.18   0.031     .0199132    .4164953
         2001  |   .1832091   .0850914     2.15   0.033     .0151411    .3512772
         2002  |   .1039546   .0684578     1.52   0.129    -.0305279    .2384371
         2003  |   .1261641   .0652947     1.93   0.054    -.0020083    .2543366
         2004  |   .1937412    .065644     2.95   0.003     .0648815    .3226009
         2005  |    .248336   .0661401     3.75   0.000     .1185017    .3781702
         2006  |   .2448769   .0672421     3.64   0.000      .112879    .3768748
         2007  |   .3071655   .0676781     4.54   0.000     .1743114    .4400196
         2008  |   .3110142   .0689474     4.51   0.000     .1756679    .4463606
         2009  |    .317169   .0680106     4.66   0.000     .1836647    .4506733
         2010  |   .3221377   .0688645     4.68   0.000     .1869539    .4573216
               |
        region |
            2  |   .0674586   .0861354     0.78   0.434    -.1016479     .236565
            3  |   .3373376   .0873338     3.86   0.000     .1658613    .5088138
            4  |   .1209224   .0919246     1.32   0.189    -.0595727    .3014176
            5  |   .1829619   .0855916     2.14   0.033     .0148699    .3510538
            6  |   .3036661   .1053146     2.88   0.004     .0968753     .510457
            7  |   .3671963   .0576714     6.37   0.000     .2539428    .4804499
            8  |   .4296462   .0798822     5.38   0.000     .2727376    .5865547
            9  |   .4409378   .0688506     6.40   0.000     .3057179    .5761577
           10  |    .329138   .0799428     4.12   0.000     .1721002    .4861758
               |
         _cons |   3.201694   .2735903    11.70   0.000     2.662817    3.740571
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
     wb_corr |
         L1. |   .3684144      .305   .3528614   .3669096   .3868883      .431
             |
    pctwomen |  -.1933377     -.221   -.202195  -.1938271  -.1827497     -.162
press3_inv~e |   .0404393     .0105   .0293046   .0423617   .0527926      .061
womenXpress3 |  -.1501188     -.177    -.15755  -.1487962  -.1436471     -.124
      fh_neg |  -.1329852     -.169  -.1454037  -.1330579  -.1197157    -.0922
     log_gdp |  -.3827678     -.427  -.3960108  -.3798291  -.3693593     -.354
pct_protes~t |  -.0416379    -.0541  -.0460089  -.0428901  -.0373241    -.0277
trade_impexp |   .0150317    .00312   .0115625   .0143787   .0189027     .0313
       wecon |  -.0158455    -.0265  -.0197754  -.0156851  -.0124497   -.00313
             |
        year |
       1996  |  -.0169932    -.0356    -.02162   -.018495  -.0125389    .00327
       1997  |   .0533932     .0204   .0393359   .0561449   .0641444     .0921
       1998  |  -.0116884    -.0241   -.016565  -.0110857  -.0087944    .00112
       1999  |   .0522123     .0135   .0386591   .0511821   .0661516     .0979
       2001  |   .0441066    .00473   .0353641   .0464883    .052937     .0722
       2002  |    .025031     .0103   .0204192   .0258024   .0303665     .0395
       2003  |   .0303762       .02   .0271588   .0295556   .0342157     .0422
       2004  |   .0466465     .0363   .0432402   .0454139   .0502349     .0581
       2005  |    .059791     .0488   .0560378   .0587977   .0633199     .0705
       2006  |   .0585968     .0474   .0545291   .0582506   .0622984     .0686
       2007  |   .0730459      .061   .0692754    .072835   .0756493     .0836
       2008  |   .0739609      .062   .0701629   .0738972   .0772448     .0847
       2009  |     .07495     .0636   .0711565   .0745371    .077875     .0854
       2010  |   .0761238     .0641   .0724299      .0756   .0788731     .0866
             |
      region |
          2  |   .0121329     .0012   .0086791   .0112129   .0146844     .0274
          3  |    .063422      .054   .0587265   .0626208   .0673826     .0783
          4  |   .0214852     .0083   .0173692   .0206673   .0264095     .0325
          5  |   .0344036     .0176   .0296613   .0340655    .037933     .0522
          6  |   .0469493     .0335    .043725   .0460632   .0514502     .0592
          7  |   .1497991      .128   .1443032   .1494766   .1553334      .171
          8  |   .1028149     .0883   .0976747    .102582    .107045      .125
          9  |   .1535322      .135   .1475602   .1519986   .1593133      .178
         10  |   .1346161      .103   .1268977   .1350926   .1426349      .173
-------------+----------------------------------------------------------------
    R-square |     .87333      .861   .8692785   .8741147   .8772939      .887
Adj R-square |   .8695629      .856   .8653908   .8703709   .8736446      .883
------------------------------------------------------------------------------

. 
. esttab ti_est icrg_est wbgi_est using press.rtf, replace order(L.cpi_ti L.icrg_corr L.wb_corr pctwomen press3_inverse womenXpress3 fh_neg 
> log_gdp pct_protestant trade_impexp wecon) keep(L.cpi_ti L.icrg_corr L.wb_corr pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_pro
> testant trade_impexp wecon) mtitles("TI CPI" "ICRG" "WBGI") coeflabels(L.cpi_ti "lag TI CPI" L.icrg_corr "lag ICRG" L.wb_corr "lag WBGI" p
> ctwomen "% women in lower house" press3_inverse "press freedom" womenXpress3 "% women * press freedom" fh_neg "FH Freedom" log_gdp "log GD
> P per capita" pct_protestant "% protestant" trade_impexp "trade imbalance (% of GDP)" wecon "women's economic rights" _cons "constant") no
> abbrev wrap gaps varwidth(25) align(r)
(output written to press.rtf)

. 
. 
. 
. 
. 
. 
. 
. 
. ***************************************************************************
. * parliamentary vs. presidential systems
. ***************************************************************************
. 
. 
. clear all

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * create interaction
. gen womenXpres=pctwomen*pres_new
(13 missing values generated)

. 
. ****
. * Transparency International Measure
. ****
. 
. * recode the DV
. rename cpi_ti cpi_ti_o

. gen cpi_ti = 10 - cpi_ti_o
(739 missing values generated)

. 
. * scatterplot: Transparency International Measure
. twoway (scatter cpi_ti pctwomen if pres_new==0 & exclude_new!=1) (lfit cpi_ti pctwomen if pres_new==0 & exclude_new!=1), title("Parliament
> ary Systems") xtitle("% Women in Lower House") ytitle("TI Corruption Perception Index") legend(label(1 "TI CPI") label(2 "Linear Fit")) yl
> abel(0 1 2 3 4 5 6 7 8 9 10) scheme(s2mono)

. graph export ti-prez-n.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-prez-n.emf written in Enhanced Metafile format)

. twoway (scatter cpi_ti pctwomen if pres_new==1 & exclude_new!=1) (lfit cpi_ti pctwomen if pres_new==1 & exclude_new!=1), title("Presidenti
> al Systems") xtitle("% Women in Lower House") ytitle("TI Corruption Perception Index") legend(label(1 "TI CPI") label(2 "Linear Fit")) yla
> bel(0 1 2 3 4 5 6 7 8 9 10) scheme(s2mono)

. graph export ti-prez-y.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-prez-y.emf written in Enhanced Metafile format)

. 
. reg cpi_ti pctwomen pres_new womenXpres if(exclude_new!=1)

      Source |       SS           df       MS      Number of obs   =     1,029
-------------+----------------------------------   F(3, 1025)      =    326.24
       Model |  2753.30762         3  917.769206   Prob > F        =    0.0000
    Residual |  2883.53829     1,025  2.81320809   R-squared       =    0.4884
-------------+----------------------------------   Adj R-squared   =    0.4870
       Total |  5636.84591     1,028  5.48331314   Root MSE        =    1.6773

------------------------------------------------------------------------------
      cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    pctwomen |  -.1404263   .0060891   -23.06   0.000    -.1523748   -.1284777
    pres_new |  -.6392509   .2249377    -2.84   0.005    -1.080642   -.1978599
  womenXpres |   .1479232   .0122487    12.08   0.000     .1238879    .1719585
       _cons |   6.695766   .1383889    48.38   0.000     6.424208    6.967324
------------------------------------------------------------------------------

. unique countryid if e(sample)
Number of unique values of countryid is  76
Number of records is  1029

. tab year if e(sample)

       Year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1995 |         35        3.40        3.40
       1996 |         41        3.98        7.39
       1997 |         42        4.08       11.47
       1998 |         58        5.64       17.10
       1999 |         67        6.51       23.62
       2000 |         58        5.64       29.25
       2001 |         66        6.41       35.67
       2002 |         70        6.80       42.47
       2003 |         74        7.19       49.66
       2004 |         75        7.29       56.95
       2005 |         76        7.39       64.33
       2006 |         74        7.19       71.53
       2007 |         74        7.19       78.72
       2008 |         74        7.19       85.91
       2009 |         73        7.09       93.00
       2010 |         72        7.00      100.00
------------+-----------------------------------
      Total |      1,029      100.00

. 
. * generate multiple imputation data sets
. ice cpi_ti pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1994, seed(123456) m(50) savi
> ng(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,016       50.75       50.75
          1 |        228       11.39       62.14
          2 |          5        0.25       62.39
          . |        753       37.61      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
   pres_new |         | [No missing data in estimation sample]
 womenXpres |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | cpi_ti pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant trade_impexp
trade_imp~p | regress | cpi_ti pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant wecon
     cpi_ti | regress | pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant trade_impexp wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg cpi_ti l.cpi_ti pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if
>  exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3749
                                                Largest FMI       =     0.6082
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     104.36
                                                        avg       =     443.70
                                                        max       =     910.03
Model F test:       Equal FMI                   F(  33, 1079.3)   =     282.92
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6825064   .0328803    20.76   0.000     .6173061    .7477067
               |
      pctwomen |  -.0106226   .0036709    -2.89   0.004    -.0178279   -.0034174
      pres_new |  -.1597822   .1049638    -1.52   0.128    -.3658533     .046289
    womenXpres |   .0140438   .0056121     2.50   0.013     .0030258    .0250617
        fh_neg |  -.1779031   .0406991    -4.37   0.000    -.2579017   -.0979045
       log_gdp |  -.3148264   .0456975    -6.89   0.000    -.4048152   -.2248377
pct_protestant |  -.0044965   .0011854    -3.79   0.000     -.006826   -.0021671
  trade_impexp |  -.0004219   .0007239    -0.58   0.560    -.0018445    .0010008
         wecon |  -.0617992    .047319    -1.31   0.192    -.1547864    .0311879
               |
          year |
         1995  |  -.1874629   .1700142    -1.10   0.272    -.5239327    .1490069
         1996  |  -.0936778   .1748297    -0.54   0.593    -.4399881    .2526324
         1997  |   .0175044   .1566188     0.11   0.911    -.2917893    .3267981
         1998  |  -.0040414   .1704014    -0.02   0.981    -.3413846    .3333017
         1999  |  -.0827844   .1436345    -0.58   0.565    -.3658535    .2002847
         2001  |    .094741   .1675978     0.57   0.573    -.2371285    .4266105
         2002  |   .0732391    .131878     0.56   0.579    -.1861635    .3326416
         2003  |   .1076116   .1339871     0.80   0.423    -.1560236    .3712468
         2004  |   .1360227    .133326     1.02   0.308    -.1262527    .3982981
         2005  |   .1802831   .1316957     1.37   0.172    -.0786807     .439247
         2006  |   .2288744   .1348809     1.70   0.091    -.0364131    .4941619
         2007  |   .2374236   .1351133     1.76   0.080    -.0282591    .5031063
         2008  |   .2844517   .1356004     2.10   0.037     .0178693     .551034
         2009  |   .3198257   .1347172     2.37   0.018     .0549428    .5847086
         2010  |   .3387401    .138121     2.45   0.015       .06708    .6104001
               |
        region |
            2  |  -.0606759   .1444544    -0.42   0.675    -.3442836    .2229319
            3  |   .2968505    .152919     1.94   0.053    -.0034231     .597124
            4  |   .0294137    .155165     0.19   0.850    -.2751175     .333945
            5  |  -.0659964   .1474331    -0.45   0.655    -.3554242    .2234313
            6  |   .0270241   .1791494     0.15   0.880    -.3245698     .378618
            7  |   .3298452   .0989903     3.33   0.001     .1354991    .5241913
            8  |   .3895253   .1347382     2.89   0.004     .1249176     .654133
            9  |   .4701183   .1288133     3.65   0.000     .2170214    .7232151
           10  |   .3259308   .1333364     2.44   0.015     .0641233    .5877383
               |
         _cons |   3.957446    .526337     7.52   0.000     2.919372    4.995519
--------------------------------------------------------------------------------

. eststo ti_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3749
                                                Largest FMI       =     0.6082
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     104.36
                                                        avg       =     443.70
                                                        max       =     910.03
Model F test:       Equal FMI                   F(  33, 1079.3)   =     282.92
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6825064   .0328803    20.76   0.000     .6173061    .7477067
               |
      pctwomen |  -.0106226   .0036709    -2.89   0.004    -.0178279   -.0034174
      pres_new |  -.1597822   .1049638    -1.52   0.128    -.3658533     .046289
    womenXpres |   .0140438   .0056121     2.50   0.013     .0030258    .0250617
        fh_neg |  -.1779031   .0406991    -4.37   0.000    -.2579017   -.0979045
       log_gdp |  -.3148264   .0456975    -6.89   0.000    -.4048152   -.2248377
pct_protestant |  -.0044965   .0011854    -3.79   0.000     -.006826   -.0021671
  trade_impexp |  -.0004219   .0007239    -0.58   0.560    -.0018445    .0010008
         wecon |  -.0617992    .047319    -1.31   0.192    -.1547864    .0311879
               |
          year |
         1995  |  -.1874629   .1700142    -1.10   0.272    -.5239327    .1490069
         1996  |  -.0936778   .1748297    -0.54   0.593    -.4399881    .2526324
         1997  |   .0175044   .1566188     0.11   0.911    -.2917893    .3267981
         1998  |  -.0040414   .1704014    -0.02   0.981    -.3413846    .3333017
         1999  |  -.0827844   .1436345    -0.58   0.565    -.3658535    .2002847
         2001  |    .094741   .1675978     0.57   0.573    -.2371285    .4266105
         2002  |   .0732391    .131878     0.56   0.579    -.1861635    .3326416
         2003  |   .1076116   .1339871     0.80   0.423    -.1560236    .3712468
         2004  |   .1360227    .133326     1.02   0.308    -.1262527    .3982981
         2005  |   .1802831   .1316957     1.37   0.172    -.0786807     .439247
         2006  |   .2288744   .1348809     1.70   0.091    -.0364131    .4941619
         2007  |   .2374236   .1351133     1.76   0.080    -.0282591    .5031063
         2008  |   .2844517   .1356004     2.10   0.037     .0178693     .551034
         2009  |   .3198257   .1347172     2.37   0.018     .0549428    .5847086
         2010  |   .3387401    .138121     2.45   0.015       .06708    .6104001
               |
        region |
            2  |  -.0606759   .1444544    -0.42   0.675    -.3442836    .2229319
            3  |   .2968505    .152919     1.94   0.053    -.0034231     .597124
            4  |   .0294137    .155165     0.19   0.850    -.2751175     .333945
            5  |  -.0659964   .1474331    -0.45   0.655    -.3554242    .2234313
            6  |   .0270241   .1791494     0.15   0.880    -.3245698     .378618
            7  |   .3298452   .0989903     3.33   0.001     .1354991    .5241913
            8  |   .3895253   .1347382     2.89   0.004     .1249176     .654133
            9  |   .4701183   .1288133     3.65   0.000     .2170214    .7232151
           10  |   .3259308   .1333364     2.44   0.015     .0641233    .5877383
               |
         _cons |   3.957446    .526337     7.52   0.000     2.919372    4.995519
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  58800

. 
. mibeta cpi_ti l.cpi_ti pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3749
                                                Largest FMI       =     0.6072
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     104.36
                                                        avg       =     443.70
                                                        max       =     910.03
Model F test:       Equal FMI                   F(  33, 1079.3)   =     282.92
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6825064   .0328803    20.76   0.000     .6173061    .7477067
               |
      pctwomen |  -.0106226   .0036709    -2.89   0.004    -.0178279   -.0034174
      pres_new |  -.1597822   .1049638    -1.52   0.128    -.3658533     .046289
    womenXpres |   .0140438   .0056121     2.50   0.013     .0030258    .0250617
        fh_neg |  -.1779031   .0406991    -4.37   0.000    -.2579017   -.0979045
       log_gdp |  -.3148264   .0456975    -6.89   0.000    -.4048152   -.2248377
pct_protestant |  -.0044965   .0011854    -3.79   0.000     -.006826   -.0021671
  trade_impexp |  -.0004219   .0007239    -0.58   0.560    -.0018445    .0010008
         wecon |  -.0617992    .047319    -1.31   0.192    -.1547864    .0311879
               |
          year |
         1995  |  -.1874629   .1700142    -1.10   0.272    -.5239327    .1490069
         1996  |  -.0936778   .1748297    -0.54   0.593    -.4399881    .2526324
         1997  |   .0175044   .1566188     0.11   0.911    -.2917893    .3267981
         1998  |  -.0040414   .1704014    -0.02   0.981    -.3413846    .3333017
         1999  |  -.0827844   .1436345    -0.58   0.565    -.3658535    .2002847
         2001  |    .094741   .1675978     0.57   0.573    -.2371285    .4266105
         2002  |   .0732391    .131878     0.56   0.579    -.1861635    .3326416
         2003  |   .1076116   .1339871     0.80   0.423    -.1560236    .3712468
         2004  |   .1360227    .133326     1.02   0.308    -.1262527    .3982981
         2005  |   .1802831   .1316957     1.37   0.172    -.0786807     .439247
         2006  |   .2288744   .1348809     1.70   0.091    -.0364131    .4941619
         2007  |   .2374236   .1351133     1.76   0.080    -.0282591    .5031063
         2008  |   .2844517   .1356004     2.10   0.037     .0178693     .551034
         2009  |   .3198257   .1347172     2.37   0.018     .0549428    .5847086
         2010  |   .3387401    .138121     2.45   0.015       .06708    .6104001
               |
        region |
            2  |  -.0606759   .1444544    -0.42   0.675    -.3442836    .2229319
            3  |   .2968505    .152919     1.94   0.053    -.0034231     .597124
            4  |   .0294137    .155165     0.19   0.850    -.2751175     .333945
            5  |  -.0659964   .1474331    -0.45   0.655    -.3554242    .2234313
            6  |   .0270241   .1791494     0.15   0.880    -.3245698     .378618
            7  |   .3298452   .0989903     3.33   0.001     .1354991    .5241913
            8  |   .3895253   .1347382     2.89   0.004     .1249176     .654133
            9  |   .4701183   .1288133     3.65   0.000     .2170214    .7232151
           10  |   .3259308   .1333364     2.44   0.015     .0641233    .5877383
               |
         _cons |   3.957446    .526337     7.52   0.000     2.919372    4.995519
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
      cpi_ti |
         L1. |   .6854682      .626   .6682362   .6860477   .7026648      .742
             |
    pctwomen |  -.0453881    -.0562  -.0491618  -.0448191  -.0417987    -.0363
    pres_new |  -.0332695    -.0537  -.0375946  -.0336159  -.0277656    -.0171
  womenXpres |   .0505844     .0303   .0462223   .0503863   .0559605     .0725
      fh_neg |  -.0767714     -.101  -.0817987   -.074455  -.0715859     -.059
     log_gdp |   -.196832     -.234  -.2083289  -.1958571  -.1840126     -.168
pct_protes~t |  -.0505316    -.0631  -.0547298  -.0498457  -.0468129    -.0334
trade_impexp |  -.0067927    -.0204  -.0100272  -.0062838   -.003019    .00404
       wecon |  -.0173276    -.0372   -.021326  -.0182217  -.0124878   -.00603
             |
        year |
       1995  |  -.0186389     -.051  -.0272379  -.0167707  -.0102132    .00248
       1996  |  -.0092887    -.0322  -.0198481  -.0098987   .0000591     .0225
       1997  |   .0017477    -.0277  -.0050421   .0002748   .0086233     .0231
       1998  |  -.0003832     -.021  -.0104903    -.00048   .0073937     .0252
       1999  |  -.0084387    -.0311  -.0132689  -.0097118  -.0028243     .0156
       2001  |   .0098572    -.0158  -.0002394   .0098396   .0197551     .0361
       2002  |   .0076106   -.00549   .0024488   .0067045   .0115989     .0243
       2003  |    .011188   -.00626   .0060625    .011639   .0154403     .0325
       2004  |   .0141367    .00177   .0084173   .0141975   .0199166     .0311
       2005  |    .018734    .00463   .0145903   .0184936   .0231851      .037
       2006  |   .0236363      .007   .0187598   .0234194    .028996     .0423
       2007  |   .0243633     .0123   .0188389   .0250709    .029183     .0429
       2008  |   .0291898      .016   .0247241   .0291077   .0349269     .0474
       2009  |   .0326116     .0187   .0283427   .0325072   .0382317     .0514
       2010  |   .0345367     .0192   .0288359   .0342411   .0391948     .0552
             |
      region |
          2  |  -.0048705    -.0133   -.007864  -.0045172  -.0024657    .00597
          3  |   .0248103     .0141   .0215392   .0254518    .027625     .0396
          4  |   .0023344   -.00551   -.000343   .0012974   .0055679    .00995
          5  |  -.0055234    -.0155  -.0086408  -.0058098  -.0028959    .00631
          6  |   .0018559   -.00585  -.0002129   .0019342   .0041278    .00935
          7  |   .0597843     .0485   .0550274   .0593939   .0637046      .075
          8  |   .0414354     .0304   .0374132   .0407978   .0440006     .0607
          9  |   .0716698     .0537   .0663952   .0715202   .0758241      .101
         10  |   .0592095     .0438   .0527488   .0592739   .0643407     .0819
-------------+----------------------------------------------------------------
    R-square |   .9188384      .902   .9141361   .9189353   .9236322      .931
Adj R-square |   .9164931      .899   .9116549   .9165928   .9214254      .929
------------------------------------------------------------------------------

. 
. 
. 
. ****
. * ICRG Corruption Measure
. ****
. 
. 
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename icrg_corr icrg_corr_o

. gen icrg_corr = 6 - icrg_corr_o
(292 missing values generated)

. 
. * create interaction
. gen womenXpres=pctwomen*pres_new
(13 missing values generated)

. 
. * scatterplot: ICRG Measure
. twoway (scatter icrg_corr pctwomen if pres_new==0 & exclude_new!=1) (lfit icrg_corr pctwomen if pres_new==0 & exclude_new!=1), title("Parl
> iamentary Systems") xtitle("% Women in Lower House") ytitle("ICRG Corruption Score") legend(label(1 "ICRG Score") label(2 "Linear Fit")) y
> label(0 1 2 3 4 5 6) scheme(s2mono)

. *graph export icrg-prez-n.emf, replace
. twoway (scatter icrg_corr pctwomen if pres_new==1 & exclude_new!=1) (lfit icrg_corr pctwomen if pres_new==1 & exclude_new!=1), title("Pres
> idential Systems") xtitle("% Women in Lower House") ytitle("ICRG Corruption Score") legend(label(1 "ICRG Score") label(2 "Linear Fit")) yl
> abel(0 1 2 3 4 5 6) scheme(s2mono)

. *graph export icrg-prez-y.emf, replace
. 
. reg icrg_corr pctwomen pres_new womenXpres if(exclude_new!=1)

      Source |       SS           df       MS      Number of obs   =     1,494
-------------+----------------------------------   F(3, 1490)      =    198.34
       Model |  753.711155         3  251.237052   Prob > F        =    0.0000
    Residual |  1887.38548     1,490  1.26670167   R-squared       =    0.2854
-------------+----------------------------------   Adj R-squared   =    0.2839
       Total |  2641.09664     1,493  1.76898636   Root MSE        =    1.1255

------------------------------------------------------------------------------
   icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    pctwomen |  -.0622029   .0034679   -17.94   0.000    -.0690054   -.0554004
    pres_new |  -.4353082   .1127738    -3.86   0.000    -.6565206   -.2140959
  womenXpres |   .0837531   .0067846    12.34   0.000     .0704448    .0970615
       _cons |    3.30128   .0730235    45.21   0.000      3.15804    3.444519
------------------------------------------------------------------------------

. unique countryid if e(sample)
Number of unique values of countryid is  76
Number of records is  1494

. 
. 
. * generate multiple imputation data sets
. ice icrg_corr pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1, seed(123456) m(50) saving(wb_impu
> ted, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,474       73.63       73.63
          1 |         20        1.00       74.63
          . |        508       25.37      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
  icrg_corr |         | [No missing data in estimation sample]
   pctwomen |         | [No missing data in estimation sample]
   pres_new |         | [No missing data in estimation sample]
 womenXpres |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | icrg_corr pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant trade_impexp
trade_imp~p | regress | icrg_corr pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg icrg_corr l.icrg_corr pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon i.year i.reg
> ion if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0006
                                                Largest FMI       =     0.0109
                                                Complete DF       =       1379
DF adjustment:   Small sample                   DF:     min       =   1,357.55
                                                        avg       =   1,375.77
                                                        max       =   1,376.99
Model F test:       Equal FMI                   F(  37, 1377.0)   =     504.61
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8553838   .0123988    68.99   0.000     .8310611    .8797064
               |
      pctwomen |  -.0074877   .0016097    -4.65   0.000    -.0106454     -.00433
      pres_new |  -.1727737   .0438121    -3.94   0.000    -.2587195   -.0868279
    womenXpres |   .0084375   .0024135     3.50   0.000     .0037029    .0131722
        fh_neg |  -.0437037   .0159008    -2.75   0.006    -.0748962   -.0125113
       log_gdp |  -.0376993   .0143603    -2.63   0.009    -.0658698   -.0095289
pct_protestant |  -.0006915   .0004742    -1.46   0.145    -.0016216    .0002386
  trade_impexp |    .000268   .0002942     0.91   0.363    -.0003091     .000845
         wecon |   .0465143    .019812     2.35   0.019     .0076489    .0853796
               |
          year |
         1991  |  -.2116248    .063131    -3.35   0.001    -.3354682   -.0877815
         1992  |  -.3224227   .0615686    -5.24   0.000     -.443201   -.2016443
         1993  |  -.2499028   .0608237    -4.11   0.000      -.36922   -.1305856
         1994  |  -.1359374   .0599591    -2.27   0.024    -.2535585   -.0183164
         1995  |  -.0797015   .0589838    -1.35   0.177    -.1954093    .0360062
         1996  |  -.1096442    .058845    -1.86   0.063    -.2250797    .0057914
         1997  |   .0024593    .058497     0.04   0.966    -.1122936    .1172123
         1998  |  -.0170262   .0583505    -0.29   0.770    -.1314917    .0974393
         1999  |    .017657   .0582196     0.30   0.762    -.0965517    .1318657
         2001  |   .0805593   .0569238     1.42   0.157    -.0311075    .1922261
         2002  |   .4776076   .0570405     8.37   0.000     .3657119    .5895033
         2003  |   .0115011   .0579602     0.20   0.843    -.1021987    .1252009
         2004  |   .0760102   .0580911     1.31   0.191    -.0379463    .1899667
         2005  |  -.0151217   .0584158    -0.26   0.796    -.1297153     .099472
         2006  |   .1102061    .058884     1.87   0.061     -.005306    .2257182
         2007  |   .0598168   .0596472     1.00   0.316    -.0571925     .176826
         2008  |   .0508856   .0602337     0.84   0.398    -.0672741    .1690454
         2009  |   .0245318   .0598057     0.41   0.682    -.0927883     .141852
         2010  |  -.0123127   .0601524    -0.20   0.838    -.1303129    .1056875
               |
        region |
            2  |  -.0970761   .0623509    -1.56   0.120    -.2193891    .0252369
            3  |  -.0596977    .063812    -0.94   0.350     -.184877    .0654817
            4  |  -.0532938   .0680106    -0.78   0.433    -.1867095    .0801219
            5  |  -.1067423   .0635048    -1.68   0.093     -.231319    .0178343
            6  |  -.1052653   .0779705    -1.35   0.177    -.2582192    .0476885
            7  |    .034873   .0423733     0.82   0.411    -.0482502    .1179962
            8  |  -.0302316    .056183    -0.54   0.591    -.1404451    .0799819
            9  |   .0113994   .0515036     0.22   0.825    -.0896346    .1124335
           10  |  -.0871926   .0562653    -1.55   0.121    -.1975677    .0231825
               |
         _cons |   .7404295   .1546339     4.79   0.000     .4370858    1.043773
--------------------------------------------------------------------------------

. eststo icrg_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0006
                                                Largest FMI       =     0.0109
                                                Complete DF       =       1379
DF adjustment:   Small sample                   DF:     min       =   1,357.55
                                                        avg       =   1,375.77
                                                        max       =   1,376.99
Model F test:       Equal FMI                   F(  37, 1377.0)   =     504.61
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8553838   .0123988    68.99   0.000     .8310611    .8797064
               |
      pctwomen |  -.0074877   .0016097    -4.65   0.000    -.0106454     -.00433
      pres_new |  -.1727737   .0438121    -3.94   0.000    -.2587195   -.0868279
    womenXpres |   .0084375   .0024135     3.50   0.000     .0037029    .0131722
        fh_neg |  -.0437037   .0159008    -2.75   0.006    -.0748962   -.0125113
       log_gdp |  -.0376993   .0143603    -2.63   0.009    -.0658698   -.0095289
pct_protestant |  -.0006915   .0004742    -1.46   0.145    -.0016216    .0002386
  trade_impexp |    .000268   .0002942     0.91   0.363    -.0003091     .000845
         wecon |   .0465143    .019812     2.35   0.019     .0076489    .0853796
               |
          year |
         1991  |  -.2116248    .063131    -3.35   0.001    -.3354682   -.0877815
         1992  |  -.3224227   .0615686    -5.24   0.000     -.443201   -.2016443
         1993  |  -.2499028   .0608237    -4.11   0.000      -.36922   -.1305856
         1994  |  -.1359374   .0599591    -2.27   0.024    -.2535585   -.0183164
         1995  |  -.0797015   .0589838    -1.35   0.177    -.1954093    .0360062
         1996  |  -.1096442    .058845    -1.86   0.063    -.2250797    .0057914
         1997  |   .0024593    .058497     0.04   0.966    -.1122936    .1172123
         1998  |  -.0170262   .0583505    -0.29   0.770    -.1314917    .0974393
         1999  |    .017657   .0582196     0.30   0.762    -.0965517    .1318657
         2001  |   .0805593   .0569238     1.42   0.157    -.0311075    .1922261
         2002  |   .4776076   .0570405     8.37   0.000     .3657119    .5895033
         2003  |   .0115011   .0579602     0.20   0.843    -.1021987    .1252009
         2004  |   .0760102   .0580911     1.31   0.191    -.0379463    .1899667
         2005  |  -.0151217   .0584158    -0.26   0.796    -.1297153     .099472
         2006  |   .1102061    .058884     1.87   0.061     -.005306    .2257182
         2007  |   .0598168   .0596472     1.00   0.316    -.0571925     .176826
         2008  |   .0508856   .0602337     0.84   0.398    -.0672741    .1690454
         2009  |   .0245318   .0598057     0.41   0.682    -.0927883     .141852
         2010  |  -.0123127   .0601524    -0.20   0.838    -.1303129    .1056875
               |
        region |
            2  |  -.0970761   .0623509    -1.56   0.120    -.2193891    .0252369
            3  |  -.0596977    .063812    -0.94   0.350     -.184877    .0654817
            4  |  -.0532938   .0680106    -0.78   0.433    -.1867095    .0801219
            5  |  -.1067423   .0635048    -1.68   0.093     -.231319    .0178343
            6  |  -.1052653   .0779705    -1.35   0.177    -.2582192    .0476885
            7  |    .034873   .0423733     0.82   0.411    -.0482502    .1179962
            8  |  -.0302316    .056183    -0.54   0.591    -.1404451    .0799819
            9  |   .0113994   .0515036     0.22   0.825    -.0896346    .1124335
           10  |  -.0871926   .0562653    -1.55   0.121    -.1975677    .0231825
               |
         _cons |   .7404295   .1546339     4.79   0.000     .4370858    1.043773
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  70850

. 
. mibeta icrg_corr l.icrg_corr pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0006
                                                Largest FMI       =     0.0109
                                                Complete DF       =       1379
DF adjustment:   Small sample                   DF:     min       =   1,357.55
                                                        avg       =   1,375.77
                                                        max       =   1,376.99
Model F test:       Equal FMI                   F(  37, 1377.0)   =     504.61
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8553838   .0123988    68.99   0.000     .8310611    .8797064
               |
      pctwomen |  -.0074877   .0016097    -4.65   0.000    -.0106454     -.00433
      pres_new |  -.1727737   .0438121    -3.94   0.000    -.2587195   -.0868279
    womenXpres |   .0084375   .0024135     3.50   0.000     .0037029    .0131722
        fh_neg |  -.0437037   .0159008    -2.75   0.006    -.0748962   -.0125113
       log_gdp |  -.0376993   .0143603    -2.63   0.009    -.0658698   -.0095289
pct_protestant |  -.0006915   .0004742    -1.46   0.145    -.0016216    .0002386
  trade_impexp |    .000268   .0002942     0.91   0.363    -.0003091     .000845
         wecon |   .0465143    .019812     2.35   0.019     .0076489    .0853796
               |
          year |
         1991  |  -.2116248    .063131    -3.35   0.001    -.3354682   -.0877815
         1992  |  -.3224227   .0615686    -5.24   0.000     -.443201   -.2016443
         1993  |  -.2499028   .0608237    -4.11   0.000      -.36922   -.1305856
         1994  |  -.1359374   .0599591    -2.27   0.024    -.2535585   -.0183164
         1995  |  -.0797015   .0589838    -1.35   0.177    -.1954093    .0360062
         1996  |  -.1096442    .058845    -1.86   0.063    -.2250797    .0057914
         1997  |   .0024593    .058497     0.04   0.966    -.1122936    .1172123
         1998  |  -.0170262   .0583505    -0.29   0.770    -.1314917    .0974393
         1999  |    .017657   .0582196     0.30   0.762    -.0965517    .1318657
         2001  |   .0805593   .0569238     1.42   0.157    -.0311075    .1922261
         2002  |   .4776076   .0570405     8.37   0.000     .3657119    .5895033
         2003  |   .0115011   .0579602     0.20   0.843    -.1021987    .1252009
         2004  |   .0760102   .0580911     1.31   0.191    -.0379463    .1899667
         2005  |  -.0151217   .0584158    -0.26   0.796    -.1297153     .099472
         2006  |   .1102061    .058884     1.87   0.061     -.005306    .2257182
         2007  |   .0598168   .0596472     1.00   0.316    -.0571925     .176826
         2008  |   .0508856   .0602337     0.84   0.398    -.0672741    .1690454
         2009  |   .0245318   .0598057     0.41   0.682    -.0927883     .141852
         2010  |  -.0123127   .0601524    -0.20   0.838    -.1303129    .1056875
               |
        region |
            2  |  -.0970761   .0623509    -1.56   0.120    -.2193891    .0252369
            3  |  -.0596977    .063812    -0.94   0.350     -.184877    .0654817
            4  |  -.0532938   .0680106    -0.78   0.433    -.1867095    .0801219
            5  |  -.1067423   .0635048    -1.68   0.093     -.231319    .0178343
            6  |  -.1052653   .0779705    -1.35   0.177    -.2582192    .0476885
            7  |    .034873   .0423733     0.82   0.411    -.0482502    .1179962
            8  |  -.0302316    .056183    -0.54   0.591    -.1404451    .0799819
            9  |   .0113994   .0515036     0.22   0.825    -.0896346    .1124335
           10  |  -.0871926   .0562653    -1.55   0.121    -.1975677    .0231825
               |
         _cons |   .7404295   .1546339     4.79   0.000     .4370858    1.043773
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
   icrg_corr |
         L1. |   .8631367      .863   .8629609   .8631212   .8633151      .864
             |
    pctwomen |  -.0575546    -.0578   -.057647  -.0575679  -.0574651    -.0573
    pres_new |  -.0644413    -.0647  -.0645139  -.0644475  -.0643712    -.0643
  womenXpres |   .0514676     .0509   .0513201   .0514707    .051621      .052
      fh_neg |  -.0342754    -.0347  -.0343976  -.0342823  -.0341367    -.0338
     log_gdp |  -.0421802    -.0435  -.0424005  -.0421708   -.041917    -.0411
pct_protes~t |  -.0140269    -.0142   -.014084  -.0140312  -.0139641    -.0138
trade_impexp |   .0077849    .00555   .0073418   .0077855    .008234    .00975
       wecon |   .0230901     .0209     .02249   .0230045   .0238215      .025
             |
        year |
       1991  |  -.0309659    -.0311  -.0310078  -.0309675  -.0309006    -.0308
       1992  |  -.0491856    -.0495  -.0492448  -.0491734   -.049125    -.0489
       1993  |   -.039021    -.0392  -.0390508  -.0390328  -.0389729    -.0389
       1994  |  -.0217013    -.0219  -.0217629  -.0216997  -.0216371    -.0215
       1995  |  -.0129952    -.0132  -.0130337  -.0129888  -.0129459    -.0128
       1996  |  -.0178773    -.0181  -.0179341  -.0178843  -.0178185    -.0177
       1997  |   .0004037   .000223   .0003271   .0004173   .0004674   .000544
       1998  |  -.0027951     -.003  -.0028623  -.0027961  -.0027395   -.00252
       1999  |   .0028987    .00281   .0028709   .0028974   .0029253    .00305
       2001  |   .0137494     .0137   .0137297   .0137509   .0137711     .0138
       2002  |   .0815154     .0815   .0814865   .0815091   .0815377     .0816
       2003  |   .0019629    .00189   .0019324    .001958   .0019942    .00207
       2004  |    .012973     .0129   .0129527    .012971   .0129919      .013
       2005  |  -.0025809   -.00265  -.0026018  -.0025783  -.0025595    -.0025
       2006  |   .0186922     .0185   .0186242   .0186757   .0187532      .019
       2007  |   .0100815     .0099   .0100292   .0100762   .0101231     .0103
       2008  |   .0085762    .00836   .0085034   .0085861   .0086512    .00891
       2009  |   .0041081      .004   .0040839   .0041056   .0041445    .00421
       2010  |  -.0020619   -.00222   -.002103  -.0020614  -.0020236   -.00193
             |
      region |
          2  |  -.0142046    -.0145  -.0142956  -.0141906  -.0141159    -.0139
          3  |  -.0091069   -.00924  -.0091698  -.0091328   -.009053   -.00891
          4  |  -.0076607   -.00833  -.0078418  -.0076627  -.0075042   -.00681
          5  |  -.0162836    -.0165  -.0163422  -.0162854  -.0162408    -.0161
          6  |  -.0132078    -.0134  -.0132581  -.0132221  -.0131685     -.013
          7  |   .0114043     .0111    .011312   .0113992   .0115121     .0118
          8  |  -.0058109   -.00623  -.0060011  -.0058231   -.005664    -.0052
          9  |   .0029941    .00244   .0028208    .002978   .0031026    .00373
         10  |  -.0287288    -.0293  -.0289779   -.028761  -.0285508    -.0278
-------------+----------------------------------------------------------------
    R-square |   .9312594      .931   .9312408   .9312588    .931273      .931
Adj R-square |    .929415      .929   .9293959   .9294144    .929429      .929
------------------------------------------------------------------------------

. 
. 
. 
. ****
. * World Bank Governance Indicator Measure
. ****
. 
. 
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename wb_corr wb_corr_o

. gen wb_corr = 2.6 - wb_corr_o
(816 missing values generated)

. 
. * create interaction
. gen womenXpres=pctwomen*pres_new
(13 missing values generated)

. 
. * scatterplot: WBGI Measure
. twoway (scatter wb_corr pctwomen if pres_new==0 & exclude_new!=1) (lfit wb_corr pctwomen if pres_new==0 & exclude_new!=1), scheme(s2mono)

. twoway (scatter wb_corr pctwomen if pres_new==1 & exclude_new!=1) (lfit wb_corr pctwomen if pres_new==1 & exclude_new!=1), scheme(s2mono)

. 
. ice wb_corr pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1995, seed(123456) m(50) sav
> ing(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        877       43.81       43.81
          1 |        298       14.89       58.69
          2 |          5        0.25       58.94
          . |        822       41.06      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
   pres_new |         | [No missing data in estimation sample]
 womenXpres |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | wb_corr pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant trade_impexp
trade_imp~p | regress | wb_corr pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant wecon
    wb_corr | regress | pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant trade_impexp wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg wb_corr l.wb_corr pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region 
> if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.3033
                                                Largest FMI       =     0.6289
                                                Complete DF       =       1076
DF adjustment:   Small sample                   DF:     min       =      96.53
                                                        avg       =     568.45
                                                        max       =     763.53
Model F test:       Equal FMI                   F(  32, 1033.1)   =     168.44
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .3757787    .040362     9.31   0.000     .2959174    .4556401
               |
      pctwomen |  -.0099163   .0021878    -4.53   0.000     -.014212   -.0056207
      pres_new |  -.1651036   .0634332    -2.60   0.009    -.2897014   -.0405058
    womenXpres |   .0105619   .0032591     3.24   0.001     .0041628     .016961
        fh_neg |  -.1852482   .0261925    -7.07   0.000    -.2368024    -.133694
       log_gdp |  -.2616163   .0249183   -10.50   0.000    -.3106216   -.2126111
pct_protestant |  -.0030848   .0006592    -4.68   0.000    -.0043798   -.0017899
  trade_impexp |   .0001771   .0004066     0.44   0.663    -.0006214    .0009755
         wecon |  -.0436163   .0263154    -1.66   0.098    -.0952891    .0080565
               |
          year |
         1996  |  -.0839882   .0730199    -1.15   0.251    -.2274653    .0594889
         1997  |   .2798401   .0959745     2.92   0.004     .0898816    .4697985
         1998  |  -.0500265   .0703718    -0.71   0.477    -.1882251    .0881721
         1999  |   .2650707   .1034001     2.56   0.012     .0598375    .4703039
         2001  |    .239839   .0880831     2.72   0.007      .065804     .413874
         2002  |   .0994656   .0704179     1.41   0.158     -.038882    .2378132
         2003  |   .1389512    .066946     2.08   0.038     .0075307    .2703716
         2004  |   .2163574   .0671016     3.22   0.001     .0846311    .3480838
         2005  |   .2753112   .0674223     4.08   0.000     .1429547    .4076676
         2006  |   .2800376   .0684808     4.09   0.000     .1456021    .4144732
         2007  |   .3464187   .0690191     5.02   0.000     .2109267    .4819108
         2008  |   .3490945   .0703104     4.97   0.000      .211065    .4871241
         2009  |   .3648578   .0691971     5.27   0.000     .2290186     .500697
         2010  |   .3780168   .0700172     5.40   0.000     .2405643    .5154694
               |
        region |
            2  |  -.0711579   .0845892    -0.84   0.401    -.2372453    .0949294
            3  |    .243646   .0881025     2.77   0.006     .0706747    .4166173
            4  |    .033132   .0930545     0.36   0.722    -.1495673    .2158313
            5  |   .0786128   .0874657     0.90   0.369    -.0931424    .2503681
            6  |   .1425885   .1081446     1.32   0.188    -.0697514    .3549284
            7  |   .3304637   .0582698     5.67   0.000     .2160577    .4448696
            8  |   .3657498    .078784     4.64   0.000     .2110418    .5204579
            9  |   .3679373   .0725223     5.07   0.000     .2255133    .5103614
           10  |   .2283256   .0787055     2.90   0.004     .0737476    .3829037
               |
         _cons |   3.124772   .2789995    11.20   0.000     2.575469    3.674075
--------------------------------------------------------------------------------

. eststo wbgi_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.3033
                                                Largest FMI       =     0.6289
                                                Complete DF       =       1076
DF adjustment:   Small sample                   DF:     min       =      96.53
                                                        avg       =     568.45
                                                        max       =     763.53
Model F test:       Equal FMI                   F(  32, 1033.1)   =     168.44
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .3757787    .040362     9.31   0.000     .2959174    .4556401
               |
      pctwomen |  -.0099163   .0021878    -4.53   0.000     -.014212   -.0056207
      pres_new |  -.1651036   .0634332    -2.60   0.009    -.2897014   -.0405058
    womenXpres |   .0105619   .0032591     3.24   0.001     .0041628     .016961
        fh_neg |  -.1852482   .0261925    -7.07   0.000    -.2368024    -.133694
       log_gdp |  -.2616163   .0249183   -10.50   0.000    -.3106216   -.2126111
pct_protestant |  -.0030848   .0006592    -4.68   0.000    -.0043798   -.0017899
  trade_impexp |   .0001771   .0004066     0.44   0.663    -.0006214    .0009755
         wecon |  -.0436163   .0263154    -1.66   0.098    -.0952891    .0080565
               |
          year |
         1996  |  -.0839882   .0730199    -1.15   0.251    -.2274653    .0594889
         1997  |   .2798401   .0959745     2.92   0.004     .0898816    .4697985
         1998  |  -.0500265   .0703718    -0.71   0.477    -.1882251    .0881721
         1999  |   .2650707   .1034001     2.56   0.012     .0598375    .4703039
         2001  |    .239839   .0880831     2.72   0.007      .065804     .413874
         2002  |   .0994656   .0704179     1.41   0.158     -.038882    .2378132
         2003  |   .1389512    .066946     2.08   0.038     .0075307    .2703716
         2004  |   .2163574   .0671016     3.22   0.001     .0846311    .3480838
         2005  |   .2753112   .0674223     4.08   0.000     .1429547    .4076676
         2006  |   .2800376   .0684808     4.09   0.000     .1456021    .4144732
         2007  |   .3464187   .0690191     5.02   0.000     .2109267    .4819108
         2008  |   .3490945   .0703104     4.97   0.000      .211065    .4871241
         2009  |   .3648578   .0691971     5.27   0.000     .2290186     .500697
         2010  |   .3780168   .0700172     5.40   0.000     .2405643    .5154694
               |
        region |
            2  |  -.0711579   .0845892    -0.84   0.401    -.2372453    .0949294
            3  |    .243646   .0881025     2.77   0.006     .0706747    .4166173
            4  |    .033132   .0930545     0.36   0.722    -.1495673    .2158313
            5  |   .0786128   .0874657     0.90   0.369    -.0931424    .2503681
            6  |   .1425885   .1081446     1.32   0.188    -.0697514    .3549284
            7  |   .3304637   .0582698     5.67   0.000     .2160577    .4448696
            8  |   .3657498    .078784     4.64   0.000     .2110418    .5204579
            9  |   .3679373   .0725223     5.07   0.000     .2255133    .5103614
           10  |   .2283256   .0787055     2.90   0.004     .0737476    .3829037
               |
         _cons |   3.124772   .2789995    11.20   0.000     2.575469    3.674075
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  55450

. 
. mibeta wb_corr l.wb_corr pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.3033
                                                Largest FMI       =     0.6279
                                                Complete DF       =       1076
DF adjustment:   Small sample                   DF:     min       =      96.53
                                                        avg       =     568.45
                                                        max       =     763.53
Model F test:       Equal FMI                   F(  32, 1033.1)   =     168.44
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .3757787    .040362     9.31   0.000     .2959174    .4556401
               |
      pctwomen |  -.0099163   .0021878    -4.53   0.000     -.014212   -.0056207
      pres_new |  -.1651036   .0634332    -2.60   0.009    -.2897014   -.0405058
    womenXpres |   .0105619   .0032591     3.24   0.001     .0041628     .016961
        fh_neg |  -.1852482   .0261925    -7.07   0.000    -.2368024    -.133694
       log_gdp |  -.2616163   .0249183   -10.50   0.000    -.3106216   -.2126111
pct_protestant |  -.0030848   .0006592    -4.68   0.000    -.0043798   -.0017899
  trade_impexp |   .0001771   .0004066     0.44   0.663    -.0006214    .0009755
         wecon |  -.0436163   .0263154    -1.66   0.098    -.0952891    .0080565
               |
          year |
         1996  |  -.0839882   .0730199    -1.15   0.251    -.2274653    .0594889
         1997  |   .2798401   .0959745     2.92   0.004     .0898816    .4697985
         1998  |  -.0500265   .0703718    -0.71   0.477    -.1882251    .0881721
         1999  |   .2650707   .1034001     2.56   0.012     .0598375    .4703039
         2001  |    .239839   .0880831     2.72   0.007      .065804     .413874
         2002  |   .0994656   .0704179     1.41   0.158     -.038882    .2378132
         2003  |   .1389512    .066946     2.08   0.038     .0075307    .2703716
         2004  |   .2163574   .0671016     3.22   0.001     .0846311    .3480838
         2005  |   .2753112   .0674223     4.08   0.000     .1429547    .4076676
         2006  |   .2800376   .0684808     4.09   0.000     .1456021    .4144732
         2007  |   .3464187   .0690191     5.02   0.000     .2109267    .4819108
         2008  |   .3490945   .0703104     4.97   0.000      .211065    .4871241
         2009  |   .3648578   .0691971     5.27   0.000     .2290186     .500697
         2010  |   .3780168   .0700172     5.40   0.000     .2405643    .5154694
               |
        region |
            2  |  -.0711579   .0845892    -0.84   0.401    -.2372453    .0949294
            3  |    .243646   .0881025     2.77   0.006     .0706747    .4166173
            4  |    .033132   .0930545     0.36   0.722    -.1495673    .2158313
            5  |   .0786128   .0874657     0.90   0.369    -.0931424    .2503681
            6  |   .1425885   .1081446     1.32   0.188    -.0697514    .3549284
            7  |   .3304637   .0582698     5.67   0.000     .2160577    .4448696
            8  |   .3657498    .078784     4.64   0.000     .2110418    .5204579
            9  |   .3679373   .0725223     5.07   0.000     .2255133    .5103614
           10  |   .2283256   .0787055     2.90   0.004     .0737476    .3829037
               |
         _cons |   3.124772   .2789995    11.20   0.000     2.575469    3.674075
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
     wb_corr |
         L1. |   .3769811      .315   .3582412   .3760806   .3970368      .439
             |
    pctwomen |  -.0956414     -.114  -.1004876  -.0947959  -.0907032    -.0774
    pres_new |  -.0774094     -.108  -.0853286  -.0763446  -.0666994    -.0534
  womenXpres |   .0869079     .0617   .0807705    .087323   .0926202      .104
      fh_neg |  -.1782285     -.216  -.1866713  -.1794304  -.1705344     -.142
     log_gdp |  -.3673431     -.409  -.3791761   -.364034  -.3535907     -.341
pct_protes~t |  -.0780257     -.092  -.0820096  -.0783754   -.074159    -.0627
trade_impexp |   .0063936   -.00655    .002874   .0064962   .0106528     .0233
       wecon |  -.0275165    -.0381  -.0318554  -.0273559  -.0236929    -.0114
             |
        year |
       1996  |  -.0193115    -.0383  -.0242198  -.0216757  -.0139631    .00199
       1997  |   .0647303     .0208   .0549574   .0682944   .0752445      .102
       1998  |  -.0115647    -.0249  -.0163095  -.0114232  -.0086163    .00242
       1999  |   .0633481     .0255   .0493361   .0642571   .0794936      .109
       2001  |   .0576669     .0142   .0496328   .0600363   .0666692     .0877
       2002  |   .0239212    .00798   .0190976   .0244948   .0297514     .0384
       2003  |   .0334132      .023   .0299472   .0334456   .0379656     .0461
       2004  |   .0520267     .0415   .0484556   .0521199   .0564852     .0646
       2005  |   .0662029     .0553   .0624314   .0667281   .0703877     .0784
       2006  |   .0669268     .0554   .0628497   .0671759    .070839     .0783
       2007  |   .0822777      .071   .0776367   .0826578   .0856691     .0944
       2008  |    .082913     .0709   .0784788   .0830881   .0864983     .0949
       2009  |   .0861119      .075   .0821541   .0867195   .0890546      .098
       2010  |   .0892174     .0777   .0849506   .0894488   .0924603      .102
             |
      region |
          2  |  -.0127778    -.0235  -.0164163  -.0135602  -.0099409    .00289
          3  |   .0457454     .0368   .0408799   .0460356   .0499011     .0588
          4  |   .0058759   -.00687   .0014726   .0054097   .0094879      .019
          5  |   .0147621   .000219   .0106461   .0146704   .0191267     .0319
          6  |   .0220143    .00749   .0182863   .0205279   .0271223     .0342
          7  |   .1346443      .113   .1282352   .1342214    .140252      .153
          8  |   .0874137     .0726   .0828172   .0874404   .0919831      .107
          9  |   .1279463      .109   .1207798   .1268784   .1347904      .154
         10  |   .0932647     .0614   .0862044    .093882   .0997927      .129
-------------+----------------------------------------------------------------
    R-square |   .8677756      .854   .8639492   .8679513   .8716514      .882
Adj R-square |   .8638433      .849   .8599031   .8640242   .8678343      .879
------------------------------------------------------------------------------

. 
. esttab ti_est icrg_est wbgi_est using prez.rtf, replace order(L.cpi_ti L.icrg_corr L.wb_corr pctwomen pres_new womenXpres fh_neg log_gdp p
> ct_protestant trade_impexp wecon) keep(L.cpi_ti L.icrg_corr L.wb_corr pctwomen pres_new womenXpres fh_neg log_gdp pct_protestant trade_imp
> exp wecon) mtitles("TI CPI" "ICRG" "WBGI") coeflabels(L.cpi_ti "lag TI CPI" L.icrg_corr "lag ICRG" L.wb_corr "lag WBGI" pctwomen "% women 
> in lower house" pres_new "presidential system" womenXpres "% women * presidentialism" fh_neg "FH Freedom" log_gdp "log GDP per capita" pct
> _protestant "% protestant" trade_impexp "trade imbalance (% of GDP)" wecon "women's economic rights" _cons "constant") noabbrev wrap gaps 
> varwidth(25) align(r)
(output written to prez.rtf)

. 
. 
. 
. *************************************************************************** 
. * personalist vs. party systems in the lower house (pers_lower)
. ***************************************************************************
. 
. ****
. * Transparency International Measure
. ****
. 
. 
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. 
. * recode the DV
. rename cpi_ti cpi_ti_o

. gen cpi_ti = 10 - cpi_ti_o
(739 missing values generated)

. 
. * create interaction variable
. gen womenXpers=pctwomen*pers_lower
(45 missing values generated)

. 
. * scatterplots: Transparency International Corruption Measure
. twoway (scatter cpi_ti pctwomen if pers_lower<=6 & pers_lower!=. & exclude_new!=1) (lfit cpi_ti pctwomen if pers_lower<=6 & pers_lower!=. 
> & exclude_new!=1), title("Low Personalism") xtitle("% Women in Lower House") ytitle("TI Corruption Perception Index") legend(label(1 "TI C
> PI") label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6 7 8 9 10) scheme(s2mono)

. graph export ti-pers-lo.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-pers-lo.emf written in Enhanced Metafile format)

. twoway (scatter cpi_ti pctwomen if pers_lower>6 & pers_lower!=. & exclude_new!=1) (lfit cpi_ti pctwomen if pers_lower>6 & pers_lower!=. & 
> exclude_new!=1), title("High Personalism") xtitle("% Women in Lower House") ytitle("TI Corruption Perception Index") legend(label(1 "TI CP
> I") label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6 7 8 9 10) scheme(s2mono)

. graph export ti-pers-hi.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-pers-hi.emf written in Enhanced Metafile format)

. 
. gen persdum = .
(2,002 missing values generated)

. replace persdum = 0 if pers_lower<=6 & pers_lower!=.
(1,181 real changes made)

. replace persdum = 1 if pers_lower>6 & pers_lower!=.
(785 real changes made)

. gen womXpersdum = pctwomen*persdum
(45 missing values generated)

. reg cpi_ti pctwomen persdum womXpersdum if(exclude_new!=1)

      Source |       SS           df       MS      Number of obs   =     1,029
-------------+----------------------------------   F(3, 1025)      =    165.41
       Model |   1838.7672         3  612.922402   Prob > F        =    0.0000
    Residual |   3798.0787     1,025  3.70544264   R-squared       =    0.3262
-------------+----------------------------------   Adj R-squared   =    0.3242
       Total |  5636.84591     1,028  5.48331314   Root MSE        =     1.925

------------------------------------------------------------------------------
      cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    pctwomen |  -.1212881   .0069134   -17.54   0.000    -.1348541   -.1077221
     persdum |   .5002471   .2727236     1.83   0.067    -.0349132    1.035407
 womXpersdum |  -.0821135   .0166587    -4.93   0.000    -.1148026   -.0494243
       _cons |   7.154958   .1605211    44.57   0.000     6.839971    7.469946
------------------------------------------------------------------------------

. unique country if e(sample)
Number of unique values of country is  76
Number of records is  1029

. tab year if e(sample)

       Year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1995 |         35        3.40        3.40
       1996 |         41        3.98        7.39
       1997 |         42        4.08       11.47
       1998 |         58        5.64       17.10
       1999 |         67        6.51       23.62
       2000 |         58        5.64       29.25
       2001 |         66        6.41       35.67
       2002 |         70        6.80       42.47
       2003 |         74        7.19       49.66
       2004 |         75        7.29       56.95
       2005 |         76        7.39       64.33
       2006 |         74        7.19       71.53
       2007 |         74        7.19       78.72
       2008 |         74        7.19       85.91
       2009 |         73        7.09       93.00
       2010 |         72        7.00      100.00
------------+-----------------------------------
      Total |      1,029      100.00

. 
. 
. * check proportion of cases with pers_lower < 2.5
. qui reg cpi_ti l.cpi_ti pers_lower if exclude_new!=1

. gen lowpers = .
(2,002 missing values generated)

. replace lowpers = 1 if pers_lower<2.5 & e(sample)
(313 real changes made)

. replace lowpers = 0 if pers_lower>=2.5 & e(sample)
(630 real changes made)

. sum lowpers

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     lowpers |        943    .3319194    .4711521          0          1

. 
. 
. * generate multiple imputation data sets
. ice cpi_ti pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1994, seed(123456) m(50) sa
> ving(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,016       50.75       50.75
          1 |        228       11.39       62.14
          2 |          5        0.25       62.39
          . |        753       37.61      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
 pers_lower |         | [No missing data in estimation sample]
 womenXpers |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | cpi_ti pctwomen pers_lower womenXpers fh_neg log_gdp
            |         | pct_protestant trade_impexp
trade_imp~p | regress | cpi_ti pctwomen pers_lower womenXpers fh_neg log_gdp
            |         | pct_protestant wecon
     cpi_ti | regress | pctwomen pers_lower womenXpers fh_neg log_gdp
            |         | pct_protestant trade_impexp wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg cpi_ti l.cpi_ti pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region 
> if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3810
                                                Largest FMI       =     0.5960
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     108.40
                                                        avg       =     413.91
                                                        max       =     822.00
Model F test:       Equal FMI                   F(  33, 1078.0)   =     284.51
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6736132   .0325598    20.69   0.000     .6090766    .7381497
               |
      pctwomen |  -.0022989     .00435    -0.53   0.597    -.0108405    .0062428
    pers_lower |   .0111342   .0153356     0.73   0.468    -.0190119    .0412804
    womenXpers |  -.0022916   .0008798    -2.60   0.009    -.0040196   -.0005635
        fh_neg |  -.1906038   .0414843    -4.59   0.000     -.272154   -.1090535
       log_gdp |  -.3097807   .0461662    -6.71   0.000     -.400693   -.2188684
pct_protestant |  -.0053904    .001231    -4.38   0.000    -.0078102   -.0029705
  trade_impexp |  -.0010906   .0007668    -1.42   0.156    -.0025982    .0004169
         wecon |  -.0403092   .0475464    -0.85   0.397    -.1337325     .053114
               |
          year |
         1995  |  -.2476781    .171605    -1.44   0.151    -.5873047    .0919486
         1996  |   -.135881   .1762904    -0.77   0.442    -.4851059    .2133438
         1997  |  -.0079422   .1575905    -0.05   0.960    -.3191631    .3032786
         1998  |  -.0681909   .1716729    -0.40   0.692    -.4080723    .2716904
         1999  |  -.1159377   .1444408    -0.80   0.423    -.4006015    .1687262
         2001  |   .0700414   .1684805     0.42   0.678    -.2635847    .4036674
         2002  |   .0454942   .1325918     0.34   0.732    -.2153178    .3063062
         2003  |   .1006221    .134626     0.75   0.455     -.164271    .3655152
         2004  |   .1296378   .1340456     0.97   0.334    -.1340559    .3933314
         2005  |   .1799878   .1325257     1.36   0.175    -.0806136    .4405893
         2006  |   .2513462   .1362063     1.85   0.066    -.0165721    .5192645
         2007  |   .2439583   .1359291     1.79   0.074    -.0233331    .5112497
         2008  |    .309398   .1368165     2.26   0.024     .0404052    .5783908
         2009  |   .3280083   .1356262     2.42   0.016     .0613246    .5946919
         2010  |   .3383487   .1392828     2.43   0.016     .0643797    .6123177
               |
        region |
            2  |  -.1200715   .1399083    -0.86   0.391    -.3947747    .1546318
            3  |   .2345764   .1571009     1.49   0.136    -.0739704    .5431231
            4  |   .1085252   .1599537     0.68   0.498    -.2054642    .4225145
            5  |   .0323545   .1440118     0.22   0.822    -.2503877    .3150967
            6  |  -.1245923   .1839021    -0.68   0.498    -.4855653    .2363806
            7  |   .2917512   .0995825     2.93   0.003     .0962552    .4872472
            8  |   .4482464   .1325778     3.38   0.001     .1878743    .7086186
            9  |   .4101625   .1195787     3.43   0.001     .1752555    .6450694
           10  |    .189634   .1294745     1.46   0.143    -.0645548    .4438227
               |
         _cons |   4.016779   .5297592     7.58   0.000     2.971888    5.061669
--------------------------------------------------------------------------------

. 
. eststo ti_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3810
                                                Largest FMI       =     0.5960
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     108.40
                                                        avg       =     413.91
                                                        max       =     822.00
Model F test:       Equal FMI                   F(  33, 1078.0)   =     284.51
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6736132   .0325598    20.69   0.000     .6090766    .7381497
               |
      pctwomen |  -.0022989     .00435    -0.53   0.597    -.0108405    .0062428
    pers_lower |   .0111342   .0153356     0.73   0.468    -.0190119    .0412804
    womenXpers |  -.0022916   .0008798    -2.60   0.009    -.0040196   -.0005635
        fh_neg |  -.1906038   .0414843    -4.59   0.000     -.272154   -.1090535
       log_gdp |  -.3097807   .0461662    -6.71   0.000     -.400693   -.2188684
pct_protestant |  -.0053904    .001231    -4.38   0.000    -.0078102   -.0029705
  trade_impexp |  -.0010906   .0007668    -1.42   0.156    -.0025982    .0004169
         wecon |  -.0403092   .0475464    -0.85   0.397    -.1337325     .053114
               |
          year |
         1995  |  -.2476781    .171605    -1.44   0.151    -.5873047    .0919486
         1996  |   -.135881   .1762904    -0.77   0.442    -.4851059    .2133438
         1997  |  -.0079422   .1575905    -0.05   0.960    -.3191631    .3032786
         1998  |  -.0681909   .1716729    -0.40   0.692    -.4080723    .2716904
         1999  |  -.1159377   .1444408    -0.80   0.423    -.4006015    .1687262
         2001  |   .0700414   .1684805     0.42   0.678    -.2635847    .4036674
         2002  |   .0454942   .1325918     0.34   0.732    -.2153178    .3063062
         2003  |   .1006221    .134626     0.75   0.455     -.164271    .3655152
         2004  |   .1296378   .1340456     0.97   0.334    -.1340559    .3933314
         2005  |   .1799878   .1325257     1.36   0.175    -.0806136    .4405893
         2006  |   .2513462   .1362063     1.85   0.066    -.0165721    .5192645
         2007  |   .2439583   .1359291     1.79   0.074    -.0233331    .5112497
         2008  |    .309398   .1368165     2.26   0.024     .0404052    .5783908
         2009  |   .3280083   .1356262     2.42   0.016     .0613246    .5946919
         2010  |   .3383487   .1392828     2.43   0.016     .0643797    .6123177
               |
        region |
            2  |  -.1200715   .1399083    -0.86   0.391    -.3947747    .1546318
            3  |   .2345764   .1571009     1.49   0.136    -.0739704    .5431231
            4  |   .1085252   .1599537     0.68   0.498    -.2054642    .4225145
            5  |   .0323545   .1440118     0.22   0.822    -.2503877    .3150967
            6  |  -.1245923   .1839021    -0.68   0.498    -.4855653    .2363806
            7  |   .2917512   .0995825     2.93   0.003     .0962552    .4872472
            8  |   .4482464   .1325778     3.38   0.001     .1878743    .7086186
            9  |   .4101625   .1195787     3.43   0.001     .1752555    .6450694
           10  |    .189634   .1294745     1.46   0.143    -.0645548    .4438227
               |
         _cons |   4.016779   .5297592     7.58   0.000     2.971888    5.061669
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  58800

. 
. mibeta cpi_ti l.cpi_ti pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3810
                                                Largest FMI       =     0.5951
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     108.40
                                                        avg       =     413.91
                                                        max       =     822.00
Model F test:       Equal FMI                   F(  33, 1078.0)   =     284.51
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6736132   .0325598    20.69   0.000     .6090766    .7381497
               |
      pctwomen |  -.0022989     .00435    -0.53   0.597    -.0108405    .0062428
    pers_lower |   .0111342   .0153356     0.73   0.468    -.0190119    .0412804
    womenXpers |  -.0022916   .0008798    -2.60   0.009    -.0040196   -.0005635
        fh_neg |  -.1906038   .0414843    -4.59   0.000     -.272154   -.1090535
       log_gdp |  -.3097807   .0461662    -6.71   0.000     -.400693   -.2188684
pct_protestant |  -.0053904    .001231    -4.38   0.000    -.0078102   -.0029705
  trade_impexp |  -.0010906   .0007668    -1.42   0.156    -.0025982    .0004169
         wecon |  -.0403092   .0475464    -0.85   0.397    -.1337325     .053114
               |
          year |
         1995  |  -.2476781    .171605    -1.44   0.151    -.5873047    .0919486
         1996  |   -.135881   .1762904    -0.77   0.442    -.4851059    .2133438
         1997  |  -.0079422   .1575905    -0.05   0.960    -.3191631    .3032786
         1998  |  -.0681909   .1716729    -0.40   0.692    -.4080723    .2716904
         1999  |  -.1159377   .1444408    -0.80   0.423    -.4006015    .1687262
         2001  |   .0700414   .1684805     0.42   0.678    -.2635847    .4036674
         2002  |   .0454942   .1325918     0.34   0.732    -.2153178    .3063062
         2003  |   .1006221    .134626     0.75   0.455     -.164271    .3655152
         2004  |   .1296378   .1340456     0.97   0.334    -.1340559    .3933314
         2005  |   .1799878   .1325257     1.36   0.175    -.0806136    .4405893
         2006  |   .2513462   .1362063     1.85   0.066    -.0165721    .5192645
         2007  |   .2439583   .1359291     1.79   0.074    -.0233331    .5112497
         2008  |    .309398   .1368165     2.26   0.024     .0404052    .5783908
         2009  |   .3280083   .1356262     2.42   0.016     .0613246    .5946919
         2010  |   .3383487   .1392828     2.43   0.016     .0643797    .6123177
               |
        region |
            2  |  -.1200715   .1399083    -0.86   0.391    -.3947747    .1546318
            3  |   .2345764   .1571009     1.49   0.136    -.0739704    .5431231
            4  |   .1085252   .1599537     0.68   0.498    -.2054642    .4225145
            5  |   .0323545   .1440118     0.22   0.822    -.2503877    .3150967
            6  |  -.1245923   .1839021    -0.68   0.498    -.4855653    .2363806
            7  |   .2917512   .0995825     2.93   0.003     .0962552    .4872472
            8  |   .4482464   .1325778     3.38   0.001     .1878743    .7086186
            9  |   .4101625   .1195787     3.43   0.001     .1752555    .6450694
           10  |    .189634   .1294745     1.46   0.143    -.0645548    .4438227
               |
         _cons |   4.016779   .5297592     7.58   0.000     2.971888    5.061669
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
      cpi_ti |
         L1. |   .6784455      .615   .6603056   .6802333   .7005607      .731
             |
    pctwomen |  -.0097335    -.0228  -.0142297  -.0090734  -.0062465    .00751
  pers_lower |   .0180485    -.0153   .0097752   .0174432   .0249656     .0437
  womenXpers |  -.0578644    -.0781  -.0643156  -.0579063  -.0525822    -.0325
      fh_neg |   -.081512     -.108  -.0872672  -.0798012  -.0767007    -.0656
     log_gdp |  -.1919361     -.232  -.2015771  -.1912961  -.1801346     -.164
pct_protes~t |  -.0600346    -.0733  -.0655178  -.0588832   -.054703    -.0411
trade_impexp |  -.0173841    -.0323  -.0202904  -.0161856   -.013653   -.00554
       wecon |  -.0111895    -.0329  -.0153403  -.0115529  -.0059258   -.00035
             |
        year |
       1995  |   -.024398    -.0576  -.0323631  -.0222123  -.0159424   -.00258
       1996  |  -.0133569    -.0349  -.0242801  -.0148461  -.0042462      .019
       1997  |  -.0007914    -.0305  -.0076613  -.0022307   .0054116     .0205
       1998  |  -.0067329    -.0281  -.0168518  -.0073829   .0011537     .0196
       1999  |   -.011713    -.0345  -.0165978   -.012824  -.0058985     .0126
       2001  |   .0072253    -.0186  -.0032515   .0074299   .0165902     .0337
       2002  |   .0046854   -.00772  -.0004782   .0035955   .0093826     .0214
       2003  |   .0103671   -.00739   .0054008   .0108815   .0145617     .0317
       2004  |    .013352   .000642   .0081316    .013774    .019275     .0304
       2005  |   .0185351    .00397   .0142639   .0183372   .0232995     .0369
       2006  |   .0257224    .00846    .020727   .0255461   .0310872     .0444
       2007  |   .0248081     .0121   .0193067   .0250382   .0302725     .0437
       2008  |    .031463     .0175   .0265871   .0311646   .0372228     .0499
       2009  |   .0331443     .0186   .0287229   .0326979   .0384335     .0522
       2010  |   .0341863     .0182   .0282986   .0340564   .0393457     .0552
             |
      region |
          2  |  -.0095459    -.0178  -.0129497  -.0088945  -.0069193    .00118
          3  |   .0194296    .00798   .0151445   .0196873   .0230249      .034
          4  |   .0085308  -.000141   .0052983   .0078171   .0121862      .017
          5  |   .0026764   -.00947   .0001371   .0021991   .0053428     .0124
          6  |  -.0084859    -.0191  -.0108883   -.008486  -.0054555    .00079
          7  |   .0524039     .0407   .0489095   .0520574   .0556225      .068
          8  |    .047256     .0371   .0428734   .0473006   .0497485     .0652
          9  |   .0619713      .046   .0569749   .0613232   .0659312     .0858
         10  |   .0341405     .0185   .0282273   .0332383   .0389701      .055
-------------+----------------------------------------------------------------
    R-square |   .9195951      .904    .915187   .9197454   .9246193      .931
Adj R-square |   .9172716      .901   .9127362   .9174263   .9224411      .929
------------------------------------------------------------------------------

. 
. 
. 
. 
. ****
. * ICRG Corruption Measure
. ****
. 
. 
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. 
. * recode the DV
. rename icrg_corr icrg_corr_o

. gen icrg_corr = 6 - icrg_corr_o
(292 missing values generated)

. 
. * create interaction variable
. gen womenXpers=pctwomen*pers_lower
(45 missing values generated)

. 
. * scatterplots: ICRG Corruption Measure
. twoway (scatter icrg_corr pctwomen if pers_lower<=6 & pers_lower!=. & exclude_new!=1) (lfit icrg_corr pctwomen if pers_lower<=6 & pers_low
> er!=. & exclude_new!=1), title("Low Personalism") xtitle("% Women in Lower House") ytitle("ICRG Corruption Score") legend(label(1 "ICRG Sc
> ore") label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6) scheme(s2mono)

. *graph export icrg-pers-lo.emf, replace
. twoway (scatter icrg_corr pctwomen if pers_lower>6 & pers_lower!=. & exclude_new!=1) (lfit icrg_corr pctwomen if pers_lower>6 & pers_lower
> !=. & exclude_new!=1), title("High Personalism") xtitle("% Women in Lower House") ytitle("ICRG Corruption Score") legend(label(1 "ICRG Sco
> re") label(2 "Linear Fit")) ylabel(0 1 2 3 4 5 6) scheme(s2mono)

. *graph export icrg-pers-hi.emf, replace 
. 
. gen persdum = .
(2,002 missing values generated)

. replace persdum = 0 if pers_lower<=6 & pers_lower!=.
(1,181 real changes made)

. replace persdum = 1 if pers_lower>6 & pers_lower!=.
(785 real changes made)

. gen womXpersdum = pctwomen*persdum
(45 missing values generated)

. reg icrg_corr pctwomen persdum womXpersdum if(exclude_new!=1)

      Source |       SS           df       MS      Number of obs   =     1,489
-------------+----------------------------------   F(3, 1485)      =     88.75
       Model |  399.987084         3  133.329028   Prob > F        =    0.0000
    Residual |   2230.8163     1,485   1.5022332   R-squared       =    0.1520
-------------+----------------------------------   Adj R-squared   =    0.1503
       Total |  2630.80338     1,488  1.76801302   Root MSE        =    1.2257

------------------------------------------------------------------------------
   icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    pctwomen |  -.0465315   .0035707   -13.03   0.000    -.0535356   -.0395274
     persdum |   .3204008   .1332433     2.40   0.016     .0590357    .5817658
 womXpersdum |  -.0202538   .0088194    -2.30   0.022    -.0375535    -.002954
       _cons |   3.286933   .0747345    43.98   0.000     3.140337     3.43353
------------------------------------------------------------------------------

. unique country if e(sample)
Number of unique values of country is  76
Number of records is  1489

. 
. * check proportion of cases with pers_lower < 2.5
. qui reg icrg_corr l.icrg_corr pers_lower if exclude_new!=1

. gen lowpers = .
(2,002 missing values generated)

. replace lowpers = 1 if pers_lower<2.5 & e(sample)
(505 real changes made)

. replace lowpers = 0 if pers_lower>=2.5 & e(sample)
(909 real changes made)

. sum lowpers

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     lowpers |      1,414    .3571429    .4793269          0          1

. 
. * generate multiple imputation data sets
. ice icrg_corr pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1, passive(womenXpers: pctwomen*pe
> rs_lower) seed(123456) m(50) saving(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,469       73.38       73.38
          1 |         20        1.00       74.38
          2 |          5        0.25       74.63
          . |        508       25.37      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
  icrg_corr |         | [No missing data in estimation sample]
   pctwomen |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
 pers_lower | regress | icrg_corr pctwomen fh_neg log_gdp pct_protestant
            |         | trade_impexp wecon
 womenXpers |         | [Passively imputed from pctwomen*pers_lower]
      wecon | mlogit  | icrg_corr pctwomen pers_lower womenXpers fh_neg
            |         | log_gdp pct_protestant trade_impexp
trade_imp~p | regress | icrg_corr pctwomen pers_lower womenXpers fh_neg
            |         | log_gdp pct_protestant wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg icrg_corr l.icrg_corr pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon i.year i.r
> egion if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0006
                                                Largest FMI       =     0.0096
                                                Complete DF       =       1379
DF adjustment:   Small sample                   DF:     min       =   1,360.34
                                                        avg       =   1,375.57
                                                        max       =   1,376.99
Model F test:       Equal FMI                   F(  37, 1377.0)   =     501.46
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8542462    .012548    68.08   0.000     .8296311    .8788614
               |
      pctwomen |  -.0001743   .0018498    -0.09   0.925    -.0038031    .0034545
    pers_lower |   .0158292   .0058983     2.68   0.007     .0042585    .0273999
    womenXpers |  -.0010842    .000366    -2.96   0.003    -.0018021   -.0003662
        fh_neg |  -.0435488    .016009    -2.72   0.007    -.0749536   -.0121441
       log_gdp |  -.0359195   .0144243    -2.49   0.013    -.0642154   -.0076235
pct_protestant |  -.0013078   .0004833    -2.71   0.007     -.002256   -.0003597
  trade_impexp |   .0002822   .0003029     0.93   0.352    -.0003119    .0008763
         wecon |   .0526822    .019994     2.63   0.009       .01346    .0919045
               |
          year |
         1991  |  -.2101034   .0633705    -3.32   0.001    -.3344166   -.0857903
         1992  |  -.3160372   .0618092    -5.11   0.000    -.4372875   -.1947869
         1993  |  -.2438808   .0610976    -3.99   0.000    -.3637353   -.1240263
         1994  |  -.1305478   .0602008    -2.17   0.030    -.2486429   -.0124527
         1995  |  -.0759592   .0592405    -1.28   0.200    -.1921706    .0402523
         1996  |  -.1088233   .0590807    -1.84   0.066    -.2247213    .0070747
         1997  |  -.0006814   .0587036    -0.01   0.991    -.1158395    .1144768
         1998  |  -.0176164   .0585548    -0.30   0.764    -.1324826    .0972499
         1999  |   .0170316   .0584109     0.29   0.771    -.0975524    .1316157
         2001  |   .0801231   .0570888     1.40   0.161    -.0318674    .1921136
         2002  |   .4760483   .0572053     8.32   0.000     .3638294    .5882672
         2003  |   .0112328   .0581284     0.19   0.847    -.1027971    .1252627
         2004  |   .0752394   .0582464     1.29   0.197    -.0390219    .1895008
         2005  |  -.0135908   .0585627    -0.23   0.817    -.1284725    .1012909
         2006  |   .1147347   .0590188     1.94   0.052    -.0010418    .2305112
         2007  |   .0617434   .0597092     1.03   0.301    -.0553875    .1788742
         2008  |   .0568688   .0602815     0.94   0.346    -.0613847    .1751222
         2009  |   .0268235   .0598769     0.45   0.654    -.0906362    .1442833
         2010  |  -.0088972   .0601866    -0.15   0.883    -.1269645    .1091701
               |
        region |
            2  |  -.1039273   .0601501    -1.73   0.084    -.2219232    .0140685
            3  |  -.0810282   .0645382    -1.26   0.210    -.2076322    .0455758
            4  |  -.0338211   .0690979    -0.49   0.625    -.1693699    .1017277
            5  |   -.048195   .0616728    -0.78   0.435    -.1691779    .0727879
            6  |  -.0385713   .0782204    -0.49   0.622    -.1920153    .1148728
            7  |   .0199989   .0426325     0.47   0.639    -.0636329    .1036306
            8  |   .0009929   .0555204     0.02   0.986     -.107921    .1099068
            9  |   .0450191   .0482817     0.93   0.351    -.0496946    .1397328
           10  |  -.1003045   .0555255    -1.81   0.071    -.2092282    .0086192
               |
         _cons |   .5780636   .1509013     3.83   0.000     .2820419    .8740854
--------------------------------------------------------------------------------

. eststo icrg_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0006
                                                Largest FMI       =     0.0096
                                                Complete DF       =       1379
DF adjustment:   Small sample                   DF:     min       =   1,360.34
                                                        avg       =   1,375.57
                                                        max       =   1,376.99
Model F test:       Equal FMI                   F(  37, 1377.0)   =     501.46
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8542462    .012548    68.08   0.000     .8296311    .8788614
               |
      pctwomen |  -.0001743   .0018498    -0.09   0.925    -.0038031    .0034545
    pers_lower |   .0158292   .0058983     2.68   0.007     .0042585    .0273999
    womenXpers |  -.0010842    .000366    -2.96   0.003    -.0018021   -.0003662
        fh_neg |  -.0435488    .016009    -2.72   0.007    -.0749536   -.0121441
       log_gdp |  -.0359195   .0144243    -2.49   0.013    -.0642154   -.0076235
pct_protestant |  -.0013078   .0004833    -2.71   0.007     -.002256   -.0003597
  trade_impexp |   .0002822   .0003029     0.93   0.352    -.0003119    .0008763
         wecon |   .0526822    .019994     2.63   0.009       .01346    .0919045
               |
          year |
         1991  |  -.2101034   .0633705    -3.32   0.001    -.3344166   -.0857903
         1992  |  -.3160372   .0618092    -5.11   0.000    -.4372875   -.1947869
         1993  |  -.2438808   .0610976    -3.99   0.000    -.3637353   -.1240263
         1994  |  -.1305478   .0602008    -2.17   0.030    -.2486429   -.0124527
         1995  |  -.0759592   .0592405    -1.28   0.200    -.1921706    .0402523
         1996  |  -.1088233   .0590807    -1.84   0.066    -.2247213    .0070747
         1997  |  -.0006814   .0587036    -0.01   0.991    -.1158395    .1144768
         1998  |  -.0176164   .0585548    -0.30   0.764    -.1324826    .0972499
         1999  |   .0170316   .0584109     0.29   0.771    -.0975524    .1316157
         2001  |   .0801231   .0570888     1.40   0.161    -.0318674    .1921136
         2002  |   .4760483   .0572053     8.32   0.000     .3638294    .5882672
         2003  |   .0112328   .0581284     0.19   0.847    -.1027971    .1252627
         2004  |   .0752394   .0582464     1.29   0.197    -.0390219    .1895008
         2005  |  -.0135908   .0585627    -0.23   0.817    -.1284725    .1012909
         2006  |   .1147347   .0590188     1.94   0.052    -.0010418    .2305112
         2007  |   .0617434   .0597092     1.03   0.301    -.0553875    .1788742
         2008  |   .0568688   .0602815     0.94   0.346    -.0613847    .1751222
         2009  |   .0268235   .0598769     0.45   0.654    -.0906362    .1442833
         2010  |  -.0088972   .0601866    -0.15   0.883    -.1269645    .1091701
               |
        region |
            2  |  -.1039273   .0601501    -1.73   0.084    -.2219232    .0140685
            3  |  -.0810282   .0645382    -1.26   0.210    -.2076322    .0455758
            4  |  -.0338211   .0690979    -0.49   0.625    -.1693699    .1017277
            5  |   -.048195   .0616728    -0.78   0.435    -.1691779    .0727879
            6  |  -.0385713   .0782204    -0.49   0.622    -.1920153    .1148728
            7  |   .0199989   .0426325     0.47   0.639    -.0636329    .1036306
            8  |   .0009929   .0555204     0.02   0.986     -.107921    .1099068
            9  |   .0450191   .0482817     0.93   0.351    -.0496946    .1397328
           10  |  -.1003045   .0555255    -1.81   0.071    -.2092282    .0086192
               |
         _cons |   .5780636   .1509013     3.83   0.000     .2820419    .8740854
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  70850

. 
. mibeta icrg_corr l.icrg_corr pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,417
                                                Average RVI       =     0.0006
                                                Largest FMI       =     0.0096
                                                Complete DF       =       1379
DF adjustment:   Small sample                   DF:     min       =   1,360.34
                                                        avg       =   1,375.57
                                                        max       =   1,376.99
Model F test:       Equal FMI                   F(  37, 1377.0)   =     501.46
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
     icrg_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     icrg_corr |
           L1. |   .8542462    .012548    68.08   0.000     .8296311    .8788614
               |
      pctwomen |  -.0001743   .0018498    -0.09   0.925    -.0038031    .0034545
    pers_lower |   .0158292   .0058983     2.68   0.007     .0042585    .0273999
    womenXpers |  -.0010842    .000366    -2.96   0.003    -.0018021   -.0003662
        fh_neg |  -.0435488    .016009    -2.72   0.007    -.0749536   -.0121441
       log_gdp |  -.0359195   .0144243    -2.49   0.013    -.0642154   -.0076235
pct_protestant |  -.0013078   .0004833    -2.71   0.007     -.002256   -.0003597
  trade_impexp |   .0002822   .0003029     0.93   0.352    -.0003119    .0008763
         wecon |   .0526822    .019994     2.63   0.009       .01346    .0919045
               |
          year |
         1991  |  -.2101034   .0633705    -3.32   0.001    -.3344166   -.0857903
         1992  |  -.3160372   .0618092    -5.11   0.000    -.4372875   -.1947869
         1993  |  -.2438808   .0610976    -3.99   0.000    -.3637353   -.1240263
         1994  |  -.1305478   .0602008    -2.17   0.030    -.2486429   -.0124527
         1995  |  -.0759592   .0592405    -1.28   0.200    -.1921706    .0402523
         1996  |  -.1088233   .0590807    -1.84   0.066    -.2247213    .0070747
         1997  |  -.0006814   .0587036    -0.01   0.991    -.1158395    .1144768
         1998  |  -.0176164   .0585548    -0.30   0.764    -.1324826    .0972499
         1999  |   .0170316   .0584109     0.29   0.771    -.0975524    .1316157
         2001  |   .0801231   .0570888     1.40   0.161    -.0318674    .1921136
         2002  |   .4760483   .0572053     8.32   0.000     .3638294    .5882672
         2003  |   .0112328   .0581284     0.19   0.847    -.1027971    .1252627
         2004  |   .0752394   .0582464     1.29   0.197    -.0390219    .1895008
         2005  |  -.0135908   .0585627    -0.23   0.817    -.1284725    .1012909
         2006  |   .1147347   .0590188     1.94   0.052    -.0010418    .2305112
         2007  |   .0617434   .0597092     1.03   0.301    -.0553875    .1788742
         2008  |   .0568688   .0602815     0.94   0.346    -.0613847    .1751222
         2009  |   .0268235   .0598769     0.45   0.654    -.0906362    .1442833
         2010  |  -.0088972   .0601866    -0.15   0.883    -.1269645    .1091701
               |
        region |
            2  |  -.1039273   .0601501    -1.73   0.084    -.2219232    .0140685
            3  |  -.0810282   .0645382    -1.26   0.210    -.2076322    .0455758
            4  |  -.0338211   .0690979    -0.49   0.625    -.1693699    .1017277
            5  |   -.048195   .0616728    -0.78   0.435    -.1691779    .0727879
            6  |  -.0385713   .0782204    -0.49   0.622    -.1920153    .1148728
            7  |   .0199989   .0426325     0.47   0.639    -.0636329    .1036306
            8  |   .0009929   .0555204     0.02   0.986     -.107921    .1099068
            9  |   .0450191   .0482817     0.93   0.351    -.0496946    .1397328
           10  |  -.1003045   .0555255    -1.81   0.071    -.2092282    .0086192
               |
         _cons |   .5780636   .1509013     3.83   0.000     .2820419    .8740854
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
   icrg_corr |
         L1. |   .8619889      .861   .8617408   .8620153   .8621933      .863
             |
    pctwomen |    -.00134   -.00243  -.0016522  -.0013509  -.0010194  -.000453
  pers_lower |   .0464876     .0442   .0460217    .046423   .0471014     .0487
  womenXpers |  -.0494581    -.0513  -.0499226   -.049427  -.0489749    -.0477
      fh_neg |  -.0341539    -.0353  -.0343323  -.0341148  -.0339508    -.0337
     log_gdp |  -.0401888    -.0412  -.0404892  -.0402195   -.039845    -.0393
pct_protes~t |  -.0265291     -.027   -.026679  -.0265014  -.0263724     -.026
trade_impexp |   .0081867    .00641   .0077335   .0080983   .0087325     .0102
       wecon |   .0261465     .0241   .0257355   .0262048   .0265467     .0281
             |
        year |
       1991  |  -.0307433    -.0311  -.0308866  -.0307549  -.0305961    -.0304
       1992  |  -.0482115    -.0486  -.0483194  -.0481986  -.0481137    -.0479
       1993  |  -.0380806    -.0384  -.0381737  -.0380825  -.0379904    -.0378
       1994  |  -.0208409     -.021  -.0209104  -.0208353  -.0207797    -.0207
       1995  |   -.012385    -.0127  -.0124631  -.0123864  -.0123134    -.0121
       1996  |  -.0177435     -.018  -.0178139  -.0177412  -.0176845    -.0175
       1997  |  -.0001119  -.000277  -.0001614  -.0001168  -.0000648   .000133
       1998  |   -.002892   -.00306  -.0029643  -.0028838  -.0028406   -.00262
       1999  |    .002796    .00268   .0027636   .0027925   .0028263    .00297
       2001  |    .013675     .0136    .013649   .0136792   .0136964     .0137
       2002  |   .0812492     .0812   .0812207   .0812508   .0812686     .0813
       2003  |   .0019171    .00183   .0018853   .0019118   .0019461    .00203
       2004  |   .0128414     .0128   .0128173   .0128392   .0128628     .0129
       2005  |  -.0023196   -.00236  -.0023437  -.0023276   -.002293   -.00222
       2006  |   .0194603     .0193   .0194133    .019472   .0195016     .0196
       2007  |   .0104062     .0102   .0103635   .0104064   .0104513     .0106
       2008  |   .0095846    .00936    .009525   .0095892   .0096399    .00982
       2009  |   .0044918     .0044    .004464   .0044953   .0045147    .00456
       2010  |  -.0014899   -.00161   -.001539  -.0014906   -.001443   -.00137
             |
      region |
          2  |  -.0152071    -.0157  -.0153021  -.0151963  -.0150999    -.0149
          3  |  -.0123609    -.0131  -.0125679  -.0123481  -.0121734    -.0117
          4  |  -.0048616   -.00567   -.005212  -.0048041  -.0045311   -.00406
          5  |  -.0073522   -.00774  -.0074555  -.0073286  -.0072127   -.00705
          6  |  -.0048396   -.00524  -.0049055  -.0048176  -.0047513   -.00459
          7  |   .0065401    .00593   .0064783   .0065982   .0066832     .0069
          8  |   .0001908  -.000624  -.0000221   .0002032   .0004305   .000863
          9  |   .0118244      .011   .0115592   .0118776   .0120637     .0124
         10  |  -.0330489    -.0344  -.0333685  -.0329861   -.032609    -.0321
-------------+----------------------------------------------------------------
    R-square |   .9308595      .931   .9308406   .9308572   .9308785      .931
Adj R-square |   .9290044      .929    .928985    .929002   .9290239      .929
------------------------------------------------------------------------------

. 
. 
. 
. ****
. * World Bank Governance Indicator Measure
. ****
. 
. 
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename wb_corr wb_corr_o

. gen wb_corr = 2.6 - wb_corr_o
(816 missing values generated)

. 
. * create interaction variable
. gen womenXpers=pctwomen*pers_lower
(45 missing values generated)

. 
. * scatterplots: World Bank Governance Indicator Corruption Measure
. twoway (scatter wb_corr pctwomen if pers_lower<=6 & pers_lower!=. & exclude_new!=1) (lfit wb_corr pctwomen if pers_lower<=6 & pers_lower!=
> . & exclude_new!=1), scheme(s2mono)

. twoway (scatter wb_corr pctwomen if pers_lower>6 & pers_lower!=. & exclude_new!=1) (lfit wb_corr pctwomen if pers_lower>6 & pers_lower!=. 
> & exclude_new!=1), scheme(s2mono)

. 
. * generate multiple imputation data sets
. ice wb_corr pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1995, seed(123456) m(50) s
> aving(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        877       43.81       43.81
          1 |        298       14.89       58.69
          2 |          5        0.25       58.94
          . |        822       41.06      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
 pers_lower |         | [No missing data in estimation sample]
 womenXpers |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | wb_corr pctwomen pers_lower womenXpers fh_neg log_gdp
            |         | pct_protestant trade_impexp
trade_imp~p | regress | wb_corr pctwomen pers_lower womenXpers fh_neg log_gdp
            |         | pct_protestant wecon
    wb_corr | regress | pctwomen pers_lower womenXpers fh_neg log_gdp
            |         | pct_protestant trade_impexp wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate, esample(used): reg wb_corr l.wb_corr pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon i.year i.regio
> n if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.3137
                                                Largest FMI       =     0.6320
                                                Complete DF       =       1076
DF adjustment:   Small sample                   DF:     min       =      95.62
                                                        avg       =     570.11
                                                        max       =     780.34
Model F test:       Equal FMI                   F(  32, 1031.0)   =     169.24
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .3684448   .0403188     9.14   0.000     .2886615     .448228
               |
      pctwomen |  -.0016691   .0024854    -0.67   0.502    -.0065488    .0032107
    pers_lower |   .0151504   .0086822     1.74   0.082    -.0019047    .0322055
    womenXpers |  -.0017889   .0005038    -3.55   0.000    -.0027784   -.0007993
        fh_neg |  -.1901778   .0265859    -7.15   0.000    -.2425181   -.1378376
       log_gdp |  -.2580107   .0250757   -10.29   0.000    -.3073287   -.2086926
pct_protestant |  -.0037175   .0006831    -5.44   0.000      -.00506   -.0023751
  trade_impexp |  -.0001807   .0004142    -0.44   0.663    -.0009938    .0006325
         wecon |  -.0309328   .0264082    -1.17   0.242    -.0827844    .0209189
               |
          year |
         1996  |  -.1044916   .0732003    -1.43   0.154    -.2483282    .0393451
         1997  |   .3274692   .0965609     3.39   0.001     .1363303     .518608
         1998  |  -.0654518   .0703225    -0.93   0.352    -.2035526     .072649
         1999  |   .3073088   .1039622     2.96   0.004      .100935    .5136826
         2001  |   .2797071   .0892619     3.13   0.002     .1032751    .4561391
         2002  |   .1016142    .070324     1.44   0.149    -.0365484    .2397769
         2003  |   .1545715   .0668024     2.31   0.021     .0234349     .285708
         2004  |   .2327844   .0668966     3.48   0.001     .1014639     .364105
         2005  |   .2973316   .0671466     4.43   0.000     .1655206    .4291426
         2006  |   .3103527   .0681476     4.55   0.000     .1765767    .4441287
         2007  |   .3728283   .0685743     5.44   0.000      .238215    .5074417
         2008  |   .3843504   .0698295     5.50   0.000     .2472711    .5214297
         2009  |   .3897321   .0687653     5.67   0.000     .2547451     .524719
         2010  |   .4077768   .0695432     5.86   0.000     .2712592    .5442944
               |
        region |
            2  |  -.0977809   .0808058    -1.21   0.227    -.2564331    .0608713
            3  |   .2053181   .0905032     2.27   0.024     .0276045    .3830317
            4  |   .0776247   .0946084     0.82   0.412    -.1081382    .2633877
            5  |   .1595786   .0835531     1.91   0.057    -.0044653    .3236224
            6  |    .127332   .1083352     1.18   0.240     -.085366    .3400299
            7  |    .313718   .0586944     5.34   0.000     .1984719    .4289642
            8  |    .406607   .0764247     5.32   0.000     .2565476    .5566664
            9  |   .3673318    .067699     5.43   0.000     .2343917    .5002719
           10  |   .1666884   .0767702     2.17   0.030      .015916    .3174608
               |
         _cons |   3.026665   .2776599    10.90   0.000     2.479976    3.573354
--------------------------------------------------------------------------------

. 
. eststo wbgi_est: mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.3137
                                                Largest FMI       =     0.6320
                                                Complete DF       =       1076
DF adjustment:   Small sample                   DF:     min       =      95.62
                                                        avg       =     570.11
                                                        max       =     780.34
Model F test:       Equal FMI                   F(  32, 1031.0)   =     169.24
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .3684448   .0403188     9.14   0.000     .2886615     .448228
               |
      pctwomen |  -.0016691   .0024854    -0.67   0.502    -.0065488    .0032107
    pers_lower |   .0151504   .0086822     1.74   0.082    -.0019047    .0322055
    womenXpers |  -.0017889   .0005038    -3.55   0.000    -.0027784   -.0007993
        fh_neg |  -.1901778   .0265859    -7.15   0.000    -.2425181   -.1378376
       log_gdp |  -.2580107   .0250757   -10.29   0.000    -.3073287   -.2086926
pct_protestant |  -.0037175   .0006831    -5.44   0.000      -.00506   -.0023751
  trade_impexp |  -.0001807   .0004142    -0.44   0.663    -.0009938    .0006325
         wecon |  -.0309328   .0264082    -1.17   0.242    -.0827844    .0209189
               |
          year |
         1996  |  -.1044916   .0732003    -1.43   0.154    -.2483282    .0393451
         1997  |   .3274692   .0965609     3.39   0.001     .1363303     .518608
         1998  |  -.0654518   .0703225    -0.93   0.352    -.2035526     .072649
         1999  |   .3073088   .1039622     2.96   0.004      .100935    .5136826
         2001  |   .2797071   .0892619     3.13   0.002     .1032751    .4561391
         2002  |   .1016142    .070324     1.44   0.149    -.0365484    .2397769
         2003  |   .1545715   .0668024     2.31   0.021     .0234349     .285708
         2004  |   .2327844   .0668966     3.48   0.001     .1014639     .364105
         2005  |   .2973316   .0671466     4.43   0.000     .1655206    .4291426
         2006  |   .3103527   .0681476     4.55   0.000     .1765767    .4441287
         2007  |   .3728283   .0685743     5.44   0.000      .238215    .5074417
         2008  |   .3843504   .0698295     5.50   0.000     .2472711    .5214297
         2009  |   .3897321   .0687653     5.67   0.000     .2547451     .524719
         2010  |   .4077768   .0695432     5.86   0.000     .2712592    .5442944
               |
        region |
            2  |  -.0977809   .0808058    -1.21   0.227    -.2564331    .0608713
            3  |   .2053181   .0905032     2.27   0.024     .0276045    .3830317
            4  |   .0776247   .0946084     0.82   0.412    -.1081382    .2633877
            5  |   .1595786   .0835531     1.91   0.057    -.0044653    .3236224
            6  |    .127332   .1083352     1.18   0.240     -.085366    .3400299
            7  |    .313718   .0586944     5.34   0.000     .1984719    .4289642
            8  |    .406607   .0764247     5.32   0.000     .2565476    .5566664
            9  |   .3673318    .067699     5.43   0.000     .2343917    .5002719
           10  |   .1666884   .0767702     2.17   0.030      .015916    .3174608
               |
         _cons |   3.026665   .2776599    10.90   0.000     2.479976    3.573354
--------------------------------------------------------------------------------

. 
. unique countryid if used==1
Number of unique values of countryid is  76
Number of records is  55450

. 
. mibeta wb_corr l.wb_corr pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,109
                                                Average RVI       =     0.3137
                                                Largest FMI       =     0.6309
                                                Complete DF       =       1076
DF adjustment:   Small sample                   DF:     min       =      95.62
                                                        avg       =     570.11
                                                        max       =     780.34
Model F test:       Equal FMI                   F(  32, 1031.0)   =     169.24
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
       wb_corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       wb_corr |
           L1. |   .3684448   .0403188     9.14   0.000     .2886615     .448228
               |
      pctwomen |  -.0016691   .0024854    -0.67   0.502    -.0065488    .0032107
    pers_lower |   .0151504   .0086822     1.74   0.082    -.0019047    .0322055
    womenXpers |  -.0017889   .0005038    -3.55   0.000    -.0027784   -.0007993
        fh_neg |  -.1901778   .0265859    -7.15   0.000    -.2425181   -.1378376
       log_gdp |  -.2580107   .0250757   -10.29   0.000    -.3073287   -.2086926
pct_protestant |  -.0037175   .0006831    -5.44   0.000      -.00506   -.0023751
  trade_impexp |  -.0001807   .0004142    -0.44   0.663    -.0009938    .0006325
         wecon |  -.0309328   .0264082    -1.17   0.242    -.0827844    .0209189
               |
          year |
         1996  |  -.1044916   .0732003    -1.43   0.154    -.2483282    .0393451
         1997  |   .3274692   .0965609     3.39   0.001     .1363303     .518608
         1998  |  -.0654518   .0703225    -0.93   0.352    -.2035526     .072649
         1999  |   .3073088   .1039622     2.96   0.004      .100935    .5136826
         2001  |   .2797071   .0892619     3.13   0.002     .1032751    .4561391
         2002  |   .1016142    .070324     1.44   0.149    -.0365484    .2397769
         2003  |   .1545715   .0668024     2.31   0.021     .0234349     .285708
         2004  |   .2327844   .0668966     3.48   0.001     .1014639     .364105
         2005  |   .2973316   .0671466     4.43   0.000     .1655206    .4291426
         2006  |   .3103527   .0681476     4.55   0.000     .1765767    .4441287
         2007  |   .3728283   .0685743     5.44   0.000      .238215    .5074417
         2008  |   .3843504   .0698295     5.50   0.000     .2472711    .5214297
         2009  |   .3897321   .0687653     5.67   0.000     .2547451     .524719
         2010  |   .4077768   .0695432     5.86   0.000     .2712592    .5442944
               |
        region |
            2  |  -.0977809   .0808058    -1.21   0.227    -.2564331    .0608713
            3  |   .2053181   .0905032     2.27   0.024     .0276045    .3830317
            4  |   .0776247   .0946084     0.82   0.412    -.1081382    .2633877
            5  |   .1595786   .0835531     1.91   0.057    -.0044653    .3236224
            6  |    .127332   .1083352     1.18   0.240     -.085366    .3400299
            7  |    .313718   .0586944     5.34   0.000     .1984719    .4289642
            8  |    .406607   .0764247     5.32   0.000     .2565476    .5566664
            9  |   .3673318    .067699     5.43   0.000     .2343917    .5002719
           10  |   .1666884   .0767702     2.17   0.030      .015916    .3174608
               |
         _cons |   3.026665   .2776599    10.90   0.000     2.479976    3.573354
--------------------------------------------------------------------------------

Standardized coefficients and R-squared
Summary statistics over 50 imputations

             |       mean       min        p25     median        p75       max
-------------+----------------------------------------------------------------
     wb_corr |
         L1. |    .370443      .315   .3514139   .3686797    .390183      .441
             |
    pctwomen |  -.0160212    -.0331    -.02148  -.0172693   -.010623    .00248
  pers_lower |   .0556023     .0254   .0437673   .0534716   .0672743     .0848
  womenXpers |  -.1029939     -.128  -.1107939  -.1035325  -.0917311    -.0762
      fh_neg |  -.1821912     -.218  -.1920013  -.1824571  -.1745172     -.144
     log_gdp |  -.3607378     -.401  -.3732482  -.3578462  -.3460165     -.334
pct_protes~t |  -.0936346     -.111  -.0981229  -.0937133  -.0875447    -.0756
trade_impexp |   -.006481    -.0187  -.0093761  -.0062611  -.0026899    .00925
       wecon |  -.0194157    -.0306  -.0240114  -.0192649  -.0154744   -.00436
             |
        year |
       1996  |  -.0239239    -.0432  -.0291526  -.0260195  -.0187106   -.00279
       1997  |   .0754282     .0315   .0648135   .0791029   .0856586      .111
       1998  |  -.0150704    -.0282  -.0197306  -.0148992  -.0118052   -.00197
       1999  |   .0731279     .0348   .0595337   .0746154   .0899407      .121
       2001  |   .0669688     .0237   .0581898   .0689767   .0759605     .0964
       2002  |   .0243327    .00847   .0195526   .0249395   .0300935     .0385
       2003  |   .0370104     .0274   .0335276   .0368538   .0414891       .05
       2004  |   .0557379     .0461     .05241   .0554597   .0600039     .0685
       2005  |   .0711931     .0611   .0674826   .0712545   .0753163     .0833
       2006  |   .0738555      .063   .0699357   .0741354   .0774476      .085
       2007  |   .0881729     .0779   .0843355   .0885998   .0909148     .0998
       2008  |   .0908974     .0796   .0867238   .0915739   .0943957      .103
       2009  |   .0915908     .0813   .0877383   .0918337   .0945282      .103
       2010  |   .0958314     .0851   .0919829   .0959248   .0986021      .108
             |
      region |
          2  |  -.0174851    -.0265  -.0210621  -.0185587  -.0143676   -.00367
          3  |   .0383817     .0251   .0339697   .0388332   .0418005     .0539
          4  |   .0137128    .00105   .0089943   .0136684   .0180025     .0255
          5  |   .0298426     .0131   .0272234   .0290915   .0326611     .0429
          6  |   .0195795    .00546   .0153547   .0193912   .0232458     .0309
          7  |   .1272786      .105   .1210036   .1270293   .1334772      .147
          8  |    .096768     .0826   .0918996   .0968136   .1018135      .113
          9  |   .1271957       .11   .1214441   .1272674   .1346709      .147
         10  |   .0677964     .0378   .0594216   .0677713   .0739603     .0989
-------------+----------------------------------------------------------------
    R-square |    .869242      .856   .8648942   .8695126   .8726538      .884
Adj R-square |   .8653533      .852   .8608762   .8656319   .8688666      .881
------------------------------------------------------------------------------

. 
. esttab ti_est icrg_est wbgi_est using pers.rtf, replace order(L.cpi_ti L.icrg_corr L.wb_corr pctwomen pers_lower womenXpers fh_neg log_gdp
>  pct_protestant trade_impexp wecon) keep(L.cpi_ti L.icrg_corr L.wb_corr pctwomen pers_lower womenXpers fh_neg log_gdp pct_protestant trade
> _impexp wecon) mtitles("TI CPI" "ICRG" "WBGI") coeflabels(L.cpi_ti "lag TI CPI" L.icrg_corr "lag ICRG" L.wb_corr "lag WBGI" pctwomen "% wo
> men in lower house" pers_lower "personalism" womenXpers "% women * personalism" fh_neg "FH Freedom" log_gdp "log GDP per capita" pct_prote
> stant "% protestant" trade_impexp "trade imbalance (% of GDP)" wecon "women's economic rights" _cons "constant") noabbrev wrap gaps varwid
> th(25) align(r)
(output written to pers.rtf)

. 
. 
. 
. 
. 
. 
. 
. 
. *
. * Generate marginal effects plots for personalism-gender interaction
. * Code stolen with love from Brambor, Clark, and Golder (2006)
. * (all other ME plots come from the same source)
. *****************************************************************
. 
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename cpi_ti cpi_ti_o

. gen cpi_ti = 10 - cpi_ti_o
(739 missing values generated)

. 
. * create interaction variable
. gen womenXpers=pctwomen*pers_lower
(45 missing values generated)

. 
. ice cpi_ti pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1994, seed(123456) m(50) sa
> ving(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,016       50.75       50.75
          1 |        228       11.39       62.14
          2 |          5        0.25       62.39
          . |        753       37.61      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
 pers_lower |         | [No missing data in estimation sample]
 womenXpers |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | cpi_ti pctwomen pers_lower womenXpers fh_neg log_gdp
            |         | pct_protestant trade_impexp
trade_imp~p | regress | cpi_ti pctwomen pers_lower womenXpers fh_neg log_gdp
            |         | pct_protestant wecon
     cpi_ti | regress | pctwomen pers_lower womenXpers fh_neg log_gdp
            |         | pct_protestant trade_impexp wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate: reg cpi_ti l.cpi_ti pctwomen pers_lower womenXpers fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!
> =1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3810
                                                Largest FMI       =     0.5960
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     108.40
                                                        avg       =     413.91
                                                        max       =     822.00
Model F test:       Equal FMI                   F(  33, 1078.0)   =     284.51
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6736132   .0325598    20.69   0.000     .6090766    .7381497
               |
      pctwomen |  -.0022989     .00435    -0.53   0.597    -.0108405    .0062428
    pers_lower |   .0111342   .0153356     0.73   0.468    -.0190119    .0412804
    womenXpers |  -.0022916   .0008798    -2.60   0.009    -.0040196   -.0005635
        fh_neg |  -.1906038   .0414843    -4.59   0.000     -.272154   -.1090535
       log_gdp |  -.3097807   .0461662    -6.71   0.000     -.400693   -.2188684
pct_protestant |  -.0053904    .001231    -4.38   0.000    -.0078102   -.0029705
  trade_impexp |  -.0010906   .0007668    -1.42   0.156    -.0025982    .0004169
         wecon |  -.0403092   .0475464    -0.85   0.397    -.1337325     .053114
               |
          year |
         1995  |  -.2476781    .171605    -1.44   0.151    -.5873047    .0919486
         1996  |   -.135881   .1762904    -0.77   0.442    -.4851059    .2133438
         1997  |  -.0079422   .1575905    -0.05   0.960    -.3191631    .3032786
         1998  |  -.0681909   .1716729    -0.40   0.692    -.4080723    .2716904
         1999  |  -.1159377   .1444408    -0.80   0.423    -.4006015    .1687262
         2001  |   .0700414   .1684805     0.42   0.678    -.2635847    .4036674
         2002  |   .0454942   .1325918     0.34   0.732    -.2153178    .3063062
         2003  |   .1006221    .134626     0.75   0.455     -.164271    .3655152
         2004  |   .1296378   .1340456     0.97   0.334    -.1340559    .3933314
         2005  |   .1799878   .1325257     1.36   0.175    -.0806136    .4405893
         2006  |   .2513462   .1362063     1.85   0.066    -.0165721    .5192645
         2007  |   .2439583   .1359291     1.79   0.074    -.0233331    .5112497
         2008  |    .309398   .1368165     2.26   0.024     .0404052    .5783908
         2009  |   .3280083   .1356262     2.42   0.016     .0613246    .5946919
         2010  |   .3383487   .1392828     2.43   0.016     .0643797    .6123177
               |
        region |
            2  |  -.1200715   .1399083    -0.86   0.391    -.3947747    .1546318
            3  |   .2345764   .1571009     1.49   0.136    -.0739704    .5431231
            4  |   .1085252   .1599537     0.68   0.498    -.2054642    .4225145
            5  |   .0323545   .1440118     0.22   0.822    -.2503877    .3150967
            6  |  -.1245923   .1839021    -0.68   0.498    -.4855653    .2363806
            7  |   .2917512   .0995825     2.93   0.003     .0962552    .4872472
            8  |   .4482464   .1325778     3.38   0.001     .1878743    .7086186
            9  |   .4101625   .1195787     3.43   0.001     .1752555    .6450694
           10  |    .189634   .1294745     1.46   0.143    -.0645548    .4438227
               |
         _cons |   4.016779   .5297592     7.58   0.000     2.971888    5.061669
--------------------------------------------------------------------------------

. mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3810
                                                Largest FMI       =     0.5960
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     108.40
                                                        avg       =     413.91
                                                        max       =     822.00
Model F test:       Equal FMI                   F(  33, 1078.0)   =     284.51
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6736132   .0325598    20.69   0.000     .6090766    .7381497
               |
      pctwomen |  -.0022989     .00435    -0.53   0.597    -.0108405    .0062428
    pers_lower |   .0111342   .0153356     0.73   0.468    -.0190119    .0412804
    womenXpers |  -.0022916   .0008798    -2.60   0.009    -.0040196   -.0005635
        fh_neg |  -.1906038   .0414843    -4.59   0.000     -.272154   -.1090535
       log_gdp |  -.3097807   .0461662    -6.71   0.000     -.400693   -.2188684
pct_protestant |  -.0053904    .001231    -4.38   0.000    -.0078102   -.0029705
  trade_impexp |  -.0010906   .0007668    -1.42   0.156    -.0025982    .0004169
         wecon |  -.0403092   .0475464    -0.85   0.397    -.1337325     .053114
               |
          year |
         1995  |  -.2476781    .171605    -1.44   0.151    -.5873047    .0919486
         1996  |   -.135881   .1762904    -0.77   0.442    -.4851059    .2133438
         1997  |  -.0079422   .1575905    -0.05   0.960    -.3191631    .3032786
         1998  |  -.0681909   .1716729    -0.40   0.692    -.4080723    .2716904
         1999  |  -.1159377   .1444408    -0.80   0.423    -.4006015    .1687262
         2001  |   .0700414   .1684805     0.42   0.678    -.2635847    .4036674
         2002  |   .0454942   .1325918     0.34   0.732    -.2153178    .3063062
         2003  |   .1006221    .134626     0.75   0.455     -.164271    .3655152
         2004  |   .1296378   .1340456     0.97   0.334    -.1340559    .3933314
         2005  |   .1799878   .1325257     1.36   0.175    -.0806136    .4405893
         2006  |   .2513462   .1362063     1.85   0.066    -.0165721    .5192645
         2007  |   .2439583   .1359291     1.79   0.074    -.0233331    .5112497
         2008  |    .309398   .1368165     2.26   0.024     .0404052    .5783908
         2009  |   .3280083   .1356262     2.42   0.016     .0613246    .5946919
         2010  |   .3383487   .1392828     2.43   0.016     .0643797    .6123177
               |
        region |
            2  |  -.1200715   .1399083    -0.86   0.391    -.3947747    .1546318
            3  |   .2345764   .1571009     1.49   0.136    -.0739704    .5431231
            4  |   .1085252   .1599537     0.68   0.498    -.2054642    .4225145
            5  |   .0323545   .1440118     0.22   0.822    -.2503877    .3150967
            6  |  -.1245923   .1839021    -0.68   0.498    -.4855653    .2363806
            7  |   .2917512   .0995825     2.93   0.003     .0962552    .4872472
            8  |   .4482464   .1325778     3.38   0.001     .1878743    .7086186
            9  |   .4101625   .1195787     3.43   0.001     .1752555    .6450694
           10  |    .189634   .1294745     1.46   0.143    -.0645548    .4438227
               |
         _cons |   4.016779   .5297592     7.58   0.000     2.971888    5.061669
--------------------------------------------------------------------------------

. 
. 
. *     ****************************************************************  *
. *       Generate the values of Z for which you want to calculate the    *
. *       marginal effect (and standard errors) of X on Y.                *
. *     ****************************************************************  *
. 
. generate MV=((_n-1)/10)+1

. 
. replace  MV=. if _n>120
(101,982 real changes made, 101,982 to missing)

. 
. *     ****************************************************************  *
. *       Grab elements of the coefficient and variance-covariance matrix *
. *       that are required to calculate the marginal effect and standard *
. *       errors.                                                         *
. *     ****************************************************************  *
. 
. matrix b=e(b) 

. matrix V=e(V)

.  
. scalar b1=b[1,2] 

. scalar b2=b[1,3]

. scalar b3=b[1,4]

. 
. 
. scalar varb1=V[2,2] 

. scalar varb2=V[3,3] 

. scalar varb3=V[4,4]

. 
. scalar covb1b3=V[2,4] 

. scalar covb2b3=V[3,4]

. 
. scalar list b1 b2 b3 varb1 varb2 varb3 covb1b3 covb2b3
        b1 = -.00229889
        b2 =  .01113425
        b3 = -.00229158
     varb1 =  .00001892
     varb2 =  .00023518
     varb3 =  7.741e-07
   covb1b3 = -2.438e-06
   covb2b3 = -.00001133

. 
. *     ****************************************************************  *
. *       Calculate the marginal effect of X on Y for all MV values of    *
. *       the modifying variable Z.                                       *
. *     ****************************************************************  *
. 
. gen conb=b1+b3*MV if _n<130
(101,982 missing values generated)

. 
. 
. *     ****************************************************************  *
. *       Calculate the standard errors for the marginal effect of X on Y *
. *       for all MV values of the modifying variable Z.                  *
. *     ****************************************************************  *
. 
. gen conse=sqrt(varb1+varb3*(MV^2)+2*covb1b3*MV) if _n<130
(101,982 missing values generated)

. 
. 
. *     ****************************************************************  *
. *       Generate upper and lower bounds of the confidence interval.     *
. *       Specify the significance of the confidence interval.            *
. *     ****************************************************************  *
. 
. gen a=1.96*conse
(101,982 missing values generated)

.  
. gen upper=conb+a
(101,982 missing values generated)

.  
. gen lower=conb-a
(101,982 missing values generated)

. 
. *     ****************************************************************  *
. *       Graph the marginal effect of X on Y across the desired range of *
. *       the modifying variable Z.  Show the confidence interval.        *
. *     ****************************************************************  *
. 
. graph twoway line conb   MV, clwidth(medium) clcolor(blue) clcolor(black) ||   line upper  MV, clpattern(dash) clwidth(thin) clcolor(black
> ) ||   line lower  MV, clpattern(dash) clwidth(thin) clcolor(black) ||   ,   xlabel(1 2 3 4 5 6 7 8 9 10 11 12 13, labsize(2.5))  ylabel(-
> 0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01,   labsize(2.5)) yscale(noline) xscale(noline) legend(col(1) order(1 2) label(1 "Marginal Effect
>  of % Wom. in Parliament") label(2 "95% Confidence Interval") label(3 " ")) yline(0, lcolor(black)) title("Marginal Effect of % Women in P
> arliament" "on Corruption As Personalism Changes", size(4)) subtitle(" " "Dependent Variable: TI Corruption Perception Index" " ", size(3)
> ) xtitle( "Personalism Score", size(3)  ) xsca(titlegap(2)) ysca(titlegap(2)) ytitle("Marginal Effect of Women in Parliament", size(3)) sc
> heme(s2mono) graphregion(fcolor(white)) yline(0.01 -0.06, lcolor(gs14)) 

. 
. graph export ti-me-pers.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-me-pers.emf written in Enhanced Metafile format)

. 
. drop MV conb conse a upper lower

. 
. 
. 
. 
. 
. 
. 
. 
. *
. * Generate marginal effects plots for presidentialism-gender interaction
. * Code stolen with love from Brambor, Clark, and Golder (2006)
. * (all other ME plots come from the same source)
. *****************************************************************
. 
. * load in the data
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename cpi_ti cpi_ti_o

. gen cpi_ti = 10 - cpi_ti_o
(739 missing values generated)

. 
. 
. * recode the presidentialism measure
. recode pres_new 2=1
(pres_new: 0 changes made)

. 
. * create interaction
. gen womenXpres=pctwomen*pres_new
(13 missing values generated)

. 
. * generate multiple imputation data sets
. ice cpi_ti pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1994, seed(123456) m(50) savi
> ng(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,016       50.75       50.75
          1 |        228       11.39       62.14
          2 |          5        0.25       62.39
          . |        753       37.61      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
   pres_new |         | [No missing data in estimation sample]
 womenXpres |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | cpi_ti pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant trade_impexp
trade_imp~p | regress | cpi_ti pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant wecon
     cpi_ti | regress | pctwomen pres_new womenXpres fh_neg log_gdp
            |         | pct_protestant trade_impexp wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate: reg cpi_ti l.cpi_ti pctwomen pres_new womenXpres fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3749
                                                Largest FMI       =     0.6082
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     104.36
                                                        avg       =     443.70
                                                        max       =     910.03
Model F test:       Equal FMI                   F(  33, 1079.3)   =     282.92
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6825064   .0328803    20.76   0.000     .6173061    .7477067
               |
      pctwomen |  -.0106226   .0036709    -2.89   0.004    -.0178279   -.0034174
      pres_new |  -.1597822   .1049638    -1.52   0.128    -.3658533     .046289
    womenXpres |   .0140438   .0056121     2.50   0.013     .0030258    .0250617
        fh_neg |  -.1779031   .0406991    -4.37   0.000    -.2579017   -.0979045
       log_gdp |  -.3148264   .0456975    -6.89   0.000    -.4048152   -.2248377
pct_protestant |  -.0044965   .0011854    -3.79   0.000     -.006826   -.0021671
  trade_impexp |  -.0004219   .0007239    -0.58   0.560    -.0018445    .0010008
         wecon |  -.0617992    .047319    -1.31   0.192    -.1547864    .0311879
               |
          year |
         1995  |  -.1874629   .1700142    -1.10   0.272    -.5239327    .1490069
         1996  |  -.0936778   .1748297    -0.54   0.593    -.4399881    .2526324
         1997  |   .0175044   .1566188     0.11   0.911    -.2917893    .3267981
         1998  |  -.0040414   .1704014    -0.02   0.981    -.3413846    .3333017
         1999  |  -.0827844   .1436345    -0.58   0.565    -.3658535    .2002847
         2001  |    .094741   .1675978     0.57   0.573    -.2371285    .4266105
         2002  |   .0732391    .131878     0.56   0.579    -.1861635    .3326416
         2003  |   .1076116   .1339871     0.80   0.423    -.1560236    .3712468
         2004  |   .1360227    .133326     1.02   0.308    -.1262527    .3982981
         2005  |   .1802831   .1316957     1.37   0.172    -.0786807     .439247
         2006  |   .2288744   .1348809     1.70   0.091    -.0364131    .4941619
         2007  |   .2374236   .1351133     1.76   0.080    -.0282591    .5031063
         2008  |   .2844517   .1356004     2.10   0.037     .0178693     .551034
         2009  |   .3198257   .1347172     2.37   0.018     .0549428    .5847086
         2010  |   .3387401    .138121     2.45   0.015       .06708    .6104001
               |
        region |
            2  |  -.0606759   .1444544    -0.42   0.675    -.3442836    .2229319
            3  |   .2968505    .152919     1.94   0.053    -.0034231     .597124
            4  |   .0294137    .155165     0.19   0.850    -.2751175     .333945
            5  |  -.0659964   .1474331    -0.45   0.655    -.3554242    .2234313
            6  |   .0270241   .1791494     0.15   0.880    -.3245698     .378618
            7  |   .3298452   .0989903     3.33   0.001     .1354991    .5241913
            8  |   .3895253   .1347382     2.89   0.004     .1249176     .654133
            9  |   .4701183   .1288133     3.65   0.000     .2170214    .7232151
           10  |   .3259308   .1333364     2.44   0.015     .0641233    .5877383
               |
         _cons |   3.957446    .526337     7.52   0.000     2.919372    4.995519
--------------------------------------------------------------------------------

. mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3749
                                                Largest FMI       =     0.6082
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     104.36
                                                        avg       =     443.70
                                                        max       =     910.03
Model F test:       Equal FMI                   F(  33, 1079.3)   =     282.92
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6825064   .0328803    20.76   0.000     .6173061    .7477067
               |
      pctwomen |  -.0106226   .0036709    -2.89   0.004    -.0178279   -.0034174
      pres_new |  -.1597822   .1049638    -1.52   0.128    -.3658533     .046289
    womenXpres |   .0140438   .0056121     2.50   0.013     .0030258    .0250617
        fh_neg |  -.1779031   .0406991    -4.37   0.000    -.2579017   -.0979045
       log_gdp |  -.3148264   .0456975    -6.89   0.000    -.4048152   -.2248377
pct_protestant |  -.0044965   .0011854    -3.79   0.000     -.006826   -.0021671
  trade_impexp |  -.0004219   .0007239    -0.58   0.560    -.0018445    .0010008
         wecon |  -.0617992    .047319    -1.31   0.192    -.1547864    .0311879
               |
          year |
         1995  |  -.1874629   .1700142    -1.10   0.272    -.5239327    .1490069
         1996  |  -.0936778   .1748297    -0.54   0.593    -.4399881    .2526324
         1997  |   .0175044   .1566188     0.11   0.911    -.2917893    .3267981
         1998  |  -.0040414   .1704014    -0.02   0.981    -.3413846    .3333017
         1999  |  -.0827844   .1436345    -0.58   0.565    -.3658535    .2002847
         2001  |    .094741   .1675978     0.57   0.573    -.2371285    .4266105
         2002  |   .0732391    .131878     0.56   0.579    -.1861635    .3326416
         2003  |   .1076116   .1339871     0.80   0.423    -.1560236    .3712468
         2004  |   .1360227    .133326     1.02   0.308    -.1262527    .3982981
         2005  |   .1802831   .1316957     1.37   0.172    -.0786807     .439247
         2006  |   .2288744   .1348809     1.70   0.091    -.0364131    .4941619
         2007  |   .2374236   .1351133     1.76   0.080    -.0282591    .5031063
         2008  |   .2844517   .1356004     2.10   0.037     .0178693     .551034
         2009  |   .3198257   .1347172     2.37   0.018     .0549428    .5847086
         2010  |   .3387401    .138121     2.45   0.015       .06708    .6104001
               |
        region |
            2  |  -.0606759   .1444544    -0.42   0.675    -.3442836    .2229319
            3  |   .2968505    .152919     1.94   0.053    -.0034231     .597124
            4  |   .0294137    .155165     0.19   0.850    -.2751175     .333945
            5  |  -.0659964   .1474331    -0.45   0.655    -.3554242    .2234313
            6  |   .0270241   .1791494     0.15   0.880    -.3245698     .378618
            7  |   .3298452   .0989903     3.33   0.001     .1354991    .5241913
            8  |   .3895253   .1347382     2.89   0.004     .1249176     .654133
            9  |   .4701183   .1288133     3.65   0.000     .2170214    .7232151
           10  |   .3259308   .1333364     2.44   0.015     .0641233    .5877383
               |
         _cons |   3.957446    .526337     7.52   0.000     2.919372    4.995519
--------------------------------------------------------------------------------

. 
. 
. 
. 
. *     ****************************************************************  *
. *       Grab elements of the coefficient and variance-covariance matrix *
. *       that are required to calculate the marginal effect and standard *
. *       errors.                                                         *
. *     ****************************************************************  *
. 
. generate MV=_n-1

. 
. replace  MV=. if _n>2
(102,100 real changes made, 102,100 to missing)

. 
. *     ****************************************************************  *
. *       Grab elements of the coefficient and variance-covariance matrix *
. *       that are required to calculate the marginal effect and standard *
. *       errors.                                                         *
. *     ****************************************************************  *
. 
. matrix b=e(b) 

. matrix V=e(V)

.  
. scalar b1=b[1,2] 

. scalar b2=b[1,3]

. scalar b3=b[1,4]

. 
. 
. scalar varb1=V[2,2] 

. scalar varb2=V[3,3] 

. scalar varb3=V[4,4]

. 
. scalar covb1b3=V[2,4] 

. scalar covb2b3=V[3,4]

. 
. scalar list b1 b2 b3 varb1 varb2 varb3 covb1b3 covb2b3
        b1 = -.01062263
        b2 = -.15978216
        b3 =  .01404379
     varb1 =  .00001348
     varb2 =   .0110174
     varb3 =   .0000315
   covb1b3 = -.00001145
   covb2b3 = -.00041757

. 
. *     ****************************************************************  *
. *       Calculate the marginal effect of X on Y for all MV values of    *
. *       the modifying variable Z.                                       *
. *     ****************************************************************  *
. 
. gen conb=b1+b3*MV if _n<3
(102,100 missing values generated)

. 
. 
. *     ****************************************************************  *
. *       Calculate the standard errors for the marginal effect of X on Y *
. *       for all MV values of the modifying variable Z.                  *
. *     ****************************************************************  *
. 
. gen conse=sqrt(varb1+varb3*(MV^2)+2*covb1b3*MV) if _n<3
(102,100 missing values generated)

. 
. 
. *     ****************************************************************  *
. *       Generate upper and lower bounds of the confidence interval.     *
. *       Specify the significance of the confidence interval.            *
. *     ****************************************************************  *
. 
. gen a=1.96*conse
(102,100 missing values generated)

.  
. gen upper=conb+a
(102,100 missing values generated)

.  
. gen lower=conb-a
(102,100 missing values generated)

. 
. 
. generate x=_n

. replace  x=. if _n>2
(102,100 real changes made, 102,100 to missing)

. label define govts 1 "parliamentary" 2 "presidential"

. label values x govts

. eclplot conb lower upper x, xscale(range(0.5 2.5)) xlabel(1 2) ytitle("Marginal Effect of Women in Parliament", size(3)) legend(on label(1
>  "95% Confidence Interval") label(2 "Parameter estimate")) yline(0, lcolor(black)) title("Marginal Effect of % Women in Parliament" "on Co
> rruption by Government Type", size(4)) subtitle(" " "Dependent Variable: TI Corruption Perception Index" " ", size(3)) xtitle( "Government
>  Type", size(3)  ) xsca(titlegap(2)) ysca(titlegap(2)) scheme(s2mono) yline(0.02, lcolor(gs15))

. graph export ti-me-prez.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-me-prez.emf written in Enhanced Metafile format)

. 
. 
. 
. 
. 
. 
. 
. 
. *
. * Generate marginal effects plots for lag-gender interaction
. * Code stolen with love from Brambor, Clark, and Golder (2006)
. * (all other ME plots come from the same source)
. *****************************************************************
. 
. * load in the data
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename cpi_ti cpi_ti_o

. gen cpi_ti = 10 - cpi_ti_o
(739 missing values generated)

. 
. ice cpi_ti pctwomen fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1994, seed(123456) m(50) saving(wb_imputed, repla
> ce) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,016       50.75       50.75
          1 |        228       11.39       62.14
          2 |          5        0.25       62.39
          . |        753       37.61      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | cpi_ti pctwomen fh_neg log_gdp pct_protestant
            |         | trade_impexp
trade_imp~p | regress | cpi_ti pctwomen fh_neg log_gdp pct_protestant wecon
     cpi_ti | regress | pctwomen fh_neg log_gdp pct_protestant trade_impexp
            |         | wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. qui mi xeq: sort countryid year; by countryid: gen lagXwomen = l.cpi_ti * pctwomen

. 
. mi fvset base 2000 year

. mi estimate: reg cpi_ti l.cpi_ti pctwomen lagXwomen fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclude_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3368
                                                Largest FMI       =     0.6106
                                                Complete DF       =       1143
DF adjustment:   Small sample                   DF:     min       =     103.57
                                                        avg       =     411.98
                                                        max       =     948.27
Model F test:       Equal FMI                   F(  32, 1087.0)   =     300.45
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .5825146   .0388221    15.00   0.000     .5058255    .6592038
               |
      pctwomen |  -.0303404   .0062525    -4.85   0.000    -.0426327   -.0180481
     lagXwomen |   .0053126   .0012219     4.35   0.000     .0029081     .007717
        fh_neg |  -.1984829    .039235    -5.06   0.000    -.2755335   -.1214322
       log_gdp |  -.3649319   .0440323    -8.29   0.000    -.4515212   -.2783427
pct_protestant |  -.0024491   .0012444    -1.97   0.049    -.0048924   -5.82e-06
  trade_impexp |  -.0006665   .0006714    -0.99   0.321    -.0019845    .0006515
         wecon |  -.0168448    .049729    -0.34   0.735    -.1146355    .0809458
               |
          year |
         1995  |  -.2614512    .183491    -1.42   0.157    -.6253383    .1024359
         1996  |  -.1078676   .1724107    -0.63   0.533    -.4491933    .2334581
         1997  |   .0051638   .1576594     0.03   0.974    -.3061975    .3165251
         1998  |  -.0183751    .153578    -0.12   0.905    -.3214456    .2846953
         1999  |  -.0967164   .1530774    -0.63   0.528    -.3988848    .2054521
         2001  |   .0750045   .1654516     0.45   0.651    -.2524364    .4024453
         2002  |   .0491619   .1453484     0.34   0.736    -.2374135    .3357373
         2003  |   .1183833    .135278     0.88   0.382    -.1478188    .3845854
         2004  |   .1371986   .1347543     1.02   0.309    -.1279175    .4023147
         2005  |   .1912068   .1373292     1.39   0.165    -.0790673     .461481
         2006  |   .2507063   .1388021     1.81   0.072    -.0224476    .5238601
         2007  |    .243862   .1386874     1.76   0.080    -.0289842    .5167082
         2008  |    .316174   .1406207     2.25   0.025     .0395188    .5928291
         2009  |   .3254625   .1385252     2.35   0.019     .0529469    .5979781
         2010  |   .3354396   .1403342     2.39   0.017      .059352    .6115272
               |
        region |
            2  |    .170893   .1515384     1.13   0.260    -.1266867    .4684728
            3  |   .4748945   .1562516     3.04   0.002     .1680433    .7817456
            4  |   .1823539   .1554732     1.17   0.241    -.1227574    .4874653
            5  |   .1296238   .1499643     0.86   0.388    -.1648614    .4241089
            6  |   .2367772   .1806703     1.31   0.190    -.1179003    .5914547
            7  |   .4735625   .1018699     4.65   0.000     .2735354    .6735896
            8  |   .4164736   .1274527     3.27   0.001     .1662808    .6666665
            9  |   .6200539   .1238256     5.01   0.000     .3768041    .8633036
           10  |   .5313407   .1414662     3.76   0.000     .2534764    .8092051
               |
         _cons |   4.574708   .5000189     9.15   0.000     3.590147    5.559269
--------------------------------------------------------------------------------

. mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3368
                                                Largest FMI       =     0.6106
                                                Complete DF       =       1143
DF adjustment:   Small sample                   DF:     min       =     103.57
                                                        avg       =     411.98
                                                        max       =     948.27
Model F test:       Equal FMI                   F(  32, 1087.0)   =     300.45
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .5825146   .0388221    15.00   0.000     .5058255    .6592038
               |
      pctwomen |  -.0303404   .0062525    -4.85   0.000    -.0426327   -.0180481
     lagXwomen |   .0053126   .0012219     4.35   0.000     .0029081     .007717
        fh_neg |  -.1984829    .039235    -5.06   0.000    -.2755335   -.1214322
       log_gdp |  -.3649319   .0440323    -8.29   0.000    -.4515212   -.2783427
pct_protestant |  -.0024491   .0012444    -1.97   0.049    -.0048924   -5.82e-06
  trade_impexp |  -.0006665   .0006714    -0.99   0.321    -.0019845    .0006515
         wecon |  -.0168448    .049729    -0.34   0.735    -.1146355    .0809458
               |
          year |
         1995  |  -.2614512    .183491    -1.42   0.157    -.6253383    .1024359
         1996  |  -.1078676   .1724107    -0.63   0.533    -.4491933    .2334581
         1997  |   .0051638   .1576594     0.03   0.974    -.3061975    .3165251
         1998  |  -.0183751    .153578    -0.12   0.905    -.3214456    .2846953
         1999  |  -.0967164   .1530774    -0.63   0.528    -.3988848    .2054521
         2001  |   .0750045   .1654516     0.45   0.651    -.2524364    .4024453
         2002  |   .0491619   .1453484     0.34   0.736    -.2374135    .3357373
         2003  |   .1183833    .135278     0.88   0.382    -.1478188    .3845854
         2004  |   .1371986   .1347543     1.02   0.309    -.1279175    .4023147
         2005  |   .1912068   .1373292     1.39   0.165    -.0790673     .461481
         2006  |   .2507063   .1388021     1.81   0.072    -.0224476    .5238601
         2007  |    .243862   .1386874     1.76   0.080    -.0289842    .5167082
         2008  |    .316174   .1406207     2.25   0.025     .0395188    .5928291
         2009  |   .3254625   .1385252     2.35   0.019     .0529469    .5979781
         2010  |   .3354396   .1403342     2.39   0.017      .059352    .6115272
               |
        region |
            2  |    .170893   .1515384     1.13   0.260    -.1266867    .4684728
            3  |   .4748945   .1562516     3.04   0.002     .1680433    .7817456
            4  |   .1823539   .1554732     1.17   0.241    -.1227574    .4874653
            5  |   .1296238   .1499643     0.86   0.388    -.1648614    .4241089
            6  |   .2367772   .1806703     1.31   0.190    -.1179003    .5914547
            7  |   .4735625   .1018699     4.65   0.000     .2735354    .6735896
            8  |   .4164736   .1274527     3.27   0.001     .1662808    .6666665
            9  |   .6200539   .1238256     5.01   0.000     .3768041    .8633036
           10  |   .5313407   .1414662     3.76   0.000     .2534764    .8092051
               |
         _cons |   4.574708   .5000189     9.15   0.000     3.590147    5.559269
--------------------------------------------------------------------------------

. 
. 
. 
. *     ****************************************************************  *
. *       Generate the values of Z for which you want to calculate the    *
. *       marginal effect (and standard errors) of X on Y.                *
. *     ****************************************************************  *
. 
. generate MV=((_n-1)/10)

. 
. replace  MV=. if _n>101
(102,001 real changes made, 102,001 to missing)

. 
. *     ****************************************************************  *
. *       Grab elements of the coefficient and variance-covariance matrix *
. *       that are required to calculate the marginal effect and standard *
. *       errors.                                                         *
. *     ****************************************************************  *
. 
. matrix b=e(b) 

. matrix V=e(V)

.  
. scalar b1=b[1,2] 

. scalar b2=b[1,1]

. scalar b3=b[1,3]

. 
. 
. scalar varb1=V[2,2] 

. scalar varb2=V[1,1] 

. scalar varb3=V[3,3]

. 
. scalar covb1b3=V[2,3] 

. scalar covb2b3=V[1,3]

. 
. scalar list b1 b2 b3 varb1 varb2 varb3 covb1b3 covb2b3
        b1 = -.03034038
        b2 =  .58251464
        b3 =  .00531256
     varb1 =  .00003909
     varb2 =  .00150715
     varb3 =  1.493e-06
   covb1b3 = -6.667e-06
   covb2b3 = -.00003381

. 
. *     ****************************************************************  *
. *       Calculate the marginal effect of X on Y for all MV values of    *
. *       the modifying variable Z.                                       *
. *     ****************************************************************  *
. 
. gen conb=b1+b3*MV if _n<102
(102,001 missing values generated)

. 
. 
. *     ****************************************************************  *
. *       Calculate the standard errors for the marginal effect of X on Y *
. *       for all MV values of the modifying variable Z.                  *
. *     ****************************************************************  *
. 
. gen conse=sqrt(varb1+varb3*(MV^2)+2*covb1b3*MV) if _n<102
(102,001 missing values generated)

. 
. 
. *     ****************************************************************  *
. *       Generate upper and lower bounds of the confidence interval.     *
. *       Specify the significance of the confidence interval.            *
. *     ****************************************************************  *
. 
. gen a=1.96*conse
(102,001 missing values generated)

.  
. gen upper=conb+a
(102,001 missing values generated)

.  
. gen lower=conb-a
(102,001 missing values generated)

. 
. *     ****************************************************************  *
. *       Graph the marginal effect of X on Y across the desired range of *
. *       the modifying variable Z.  Show the confidence interval.        *
. *     ****************************************************************  *
. 
. graph twoway line conb   MV, clwidth(medium) clcolor(blue) clcolor(black) ||   line upper  MV, clpattern(dash) clwidth(thin) clcolor(black
> ) ||   line lower  MV, clpattern(dash) clwidth(thin) clcolor(black) ||   ,   xlabel(0 1 2 3 4 5 6 7 8 9 10, labsize(2.5))  ylabel(-0.05 -0
> .04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04,   labsize(2.5)) yscale(noline) xscale(noline) legend(col(1) order(1 2) label(1 "Marginal Effe
> ct of % Wom. in Parliament") label(2 "95% Confidence Interval") label(3 " ")) yline(0, lcolor(black)) title("Marginal Effect of % Women in
>  Parliament" "on Corruption As Lag Corruption Changes", size(4)) subtitle(" " "Dependent Variable: TI Corruption Perception Index" " ", si
> ze(3)) xtitle( "Lag TI CPI", size(3)  ) xsca(titlegap(2)) ysca(titlegap(2)) ytitle("Marginal Effect of Women in Parliament", size(3)) sche
> me(s2mono) graphregion(fcolor(white)) yline(-0.05, lcolor(gs15))

. 
. graph export ti-me-lag.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-me-lag.emf written in Enhanced Metafile format)

. 
. drop MV conb conse a upper lower

. 
. 
. 
. 
. 
. 
. 
. *
. * Generate marginal effects plots for press restrictions-gender interaction
. * Code stolen with love from Brambor, Clark, and Golder (2006)
. * (all other ME plots come from the same source)
. *****************************************************************
. 
. 
. * load in the data
. clear

. use schwindtbayer_tavits2016_TSCS_corruption_dataset_final.dta

. set more off

. 
. merge 1:1 country year using "wecon-data.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             2
        from master                         0  (_merge==1)
        from using                          2  (_merge==2)

    matched                             2,002  (_merge==3)
    -----------------------------------------

. drop if _merge==2
(2 observations deleted)

. drop _merge

. 
. * create inverse press variable 
. gen press3_inverse = (-1)*press3
(16 missing values generated)

. replace press3_inverse = . if year <= 1992
(217 real changes made, 217 to missing)

. 
. tsset countryid year
       panel variable:  countryid (unbalanced)
        time variable:  year, 1990 to 2010, but with gaps
                delta:  1 unit

. 
. * recode the DV
. rename cpi_ti cpi_ti_o

. gen cpi_ti = 10 - cpi_ti_o
(739 missing values generated)

. 
. * create interaction variable
. gen womenXpress3=pctwomen*press3_inverse
(241 missing values generated)

. 
. * generate multiple imputation data sets
. ice cpi_ti pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon if exclude_new!=1 & year>=1994, seed(123456) m(
> 50) saving(wb_imputed, replace) 

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,016       50.75       50.75
          1 |        228       11.39       62.14
          2 |          5        0.25       62.39
          . |        753       37.61      100.00
------------+-----------------------------------
      Total |      2,002      100.00

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
   pctwomen |         | [No missing data in estimation sample]
press3_in~e |         | [No missing data in estimation sample]
womenXpre~3 |         | [No missing data in estimation sample]
     fh_neg |         | [No missing data in estimation sample]
    log_gdp |         | [No missing data in estimation sample]
pct_prote~t |         | [No missing data in estimation sample]
      wecon | mlogit  | cpi_ti pctwomen press3_inverse womenXpress3 fh_neg
            |         | log_gdp pct_protestant trade_impexp
trade_imp~p | regress | cpi_ti pctwomen press3_inverse womenXpress3 fh_neg
            |         | log_gdp pct_protestant wecon
     cpi_ti | regress | pctwomen press3_inverse womenXpress3 fh_neg log_gdp
            |         | pct_protestant trade_impexp wecon
------------------------------------------------------------------------------

Imputing ..........1..........2..........3..........4..........5..........6..........7..........8..........9..........10..........11........
> ..12..........13..........14..........15..........16..........17..........18..........19..........20..........21..........22..........23..
> ........24..........25..........26..........27..........28..........29..........30..........31..........32..........33..........34........
> ..35..........36..........37..........38..........39..........40..........41..........42..........43..........44..........45..........46..
> ........47..........48..........49..........50
file wb_imputed.dta saved

. use wb_imputed, replace

. mi import ice

. 
. mi fvset base 2000 year

. mi estimate: reg cpi_ti l.cpi_ti pctwomen press3_inverse womenXpress3 fh_neg log_gdp pct_prot trade_impexp wecon i.year i.region if exclud
> e_new!=1

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3670
                                                Largest FMI       =     0.5944
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     108.96
                                                        avg       =     433.04
                                                        max       =     868.17
Model F test:       Equal FMI                   F(  33, 1081.1)   =     297.42
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6811203   .0323311    21.07   0.000     .6170407    .7451998
               |
      pctwomen |  -.0263327   .0059084    -4.46   0.000    -.0379347   -.0147307
press3_inverse |   .0095119   .0042808     2.22   0.027     .0010984    .0179254
  womenXpress3 |  -.0008022   .0001912    -4.20   0.000    -.0011777   -.0004268
        fh_neg |  -.1814418   .0616254    -2.94   0.003    -.3026884   -.0601952
       log_gdp |  -.3239139   .0452923    -7.15   0.000    -.4131167   -.2347111
pct_protestant |   -.002836   .0011922    -2.38   0.018    -.0051771   -.0004949
  trade_impexp |  -.0004086   .0006976    -0.59   0.558    -.0017795    .0009624
         wecon |  -.0383369   .0462431    -0.83   0.407    -.1291931    .0525193
               |
          year |
         1995  |  -.1774976   .1642047    -1.08   0.282    -.5023671    .1473719
         1996  |  -.0526511   .1687882    -0.31   0.756     -.386894    .2815917
         1997  |    .040876   .1517588     0.27   0.788     -.258762     .340514
         1998  |  -.0025534    .164486    -0.02   0.988    -.3280872    .3229803
         1999  |  -.0560197   .1387574    -0.40   0.687    -.3294058    .2173664
         2001  |   .1215039   .1615562     0.75   0.453    -.1982836    .4412913
         2002  |   .1056465   .1277434     0.83   0.409    -.1455811    .3568742
         2003  |    .145338   .1296392     1.12   0.263    -.1096801    .4003561
         2004  |   .1644019    .129337     1.27   0.205    -.0899705    .4187743
         2005  |   .2053502   .1280445     1.60   0.110    -.0463823    .4570827
         2006  |    .240132   .1313889     1.83   0.068    -.0182394    .4985034
         2007  |   .2520684   .1310111     1.92   0.055    -.0054867    .5096234
         2008  |   .2976509   .1319075     2.26   0.025     .0383784    .5569235
         2009  |   .3168169    .130902     2.42   0.016     .0594959    .5741379
         2010  |    .327644   .1337855     2.45   0.015     .0645962    .5906917
               |
        region |
            2  |   .1454559   .1476773     0.98   0.325    -.1444888    .4354006
            3  |   .4033952   .1502075     2.69   0.007     .1084415    .6983489
            4  |   .1663991   .1525094     1.09   0.276    -.1329311    .4657293
            5  |   .0408367   .1408036     0.29   0.772    -.2355438    .3172172
            6  |    .266744   .1747772     1.53   0.127    -.0762988    .6097869
            7  |   .4234858   .0977337     4.33   0.000     .2315778    .6153939
            8  |   .4222507   .1323493     3.19   0.001     .1623483    .6821532
            9  |   .5625251   .1225874     4.59   0.000     .3216061    .8034441
           10  |   .4505017    .133202     3.38   0.001     .1889558    .7120476
               |
         _cons |    4.05999   .5210219     7.79   0.000     3.031805    5.088174
--------------------------------------------------------------------------------

. 
. mi estimate, post

Multiple-imputation estimates                   Imputations       =         50
Linear regression                               Number of obs     =      1,176
                                                Average RVI       =     0.3670
                                                Largest FMI       =     0.5944
                                                Complete DF       =       1142
DF adjustment:   Small sample                   DF:     min       =     108.96
                                                        avg       =     433.04
                                                        max       =     868.17
Model F test:       Equal FMI                   F(  33, 1081.1)   =     297.42
Within VCE type:          OLS                   Prob > F          =     0.0000

--------------------------------------------------------------------------------
        cpi_ti |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        cpi_ti |
           L1. |   .6811203   .0323311    21.07   0.000     .6170407    .7451998
               |
      pctwomen |  -.0263327   .0059084    -4.46   0.000    -.0379347   -.0147307
press3_inverse |   .0095119   .0042808     2.22   0.027     .0010984    .0179254
  womenXpress3 |  -.0008022   .0001912    -4.20   0.000    -.0011777   -.0004268
        fh_neg |  -.1814418   .0616254    -2.94   0.003    -.3026884   -.0601952
       log_gdp |  -.3239139   .0452923    -7.15   0.000    -.4131167   -.2347111
pct_protestant |   -.002836   .0011922    -2.38   0.018    -.0051771   -.0004949
  trade_impexp |  -.0004086   .0006976    -0.59   0.558    -.0017795    .0009624
         wecon |  -.0383369   .0462431    -0.83   0.407    -.1291931    .0525193
               |
          year |
         1995  |  -.1774976   .1642047    -1.08   0.282    -.5023671    .1473719
         1996  |  -.0526511   .1687882    -0.31   0.756     -.386894    .2815917
         1997  |    .040876   .1517588     0.27   0.788     -.258762     .340514
         1998  |  -.0025534    .164486    -0.02   0.988    -.3280872    .3229803
         1999  |  -.0560197   .1387574    -0.40   0.687    -.3294058    .2173664
         2001  |   .1215039   .1615562     0.75   0.453    -.1982836    .4412913
         2002  |   .1056465   .1277434     0.83   0.409    -.1455811    .3568742
         2003  |    .145338   .1296392     1.12   0.263    -.1096801    .4003561
         2004  |   .1644019    .129337     1.27   0.205    -.0899705    .4187743
         2005  |   .2053502   .1280445     1.60   0.110    -.0463823    .4570827
         2006  |    .240132   .1313889     1.83   0.068    -.0182394    .4985034
         2007  |   .2520684   .1310111     1.92   0.055    -.0054867    .5096234
         2008  |   .2976509   .1319075     2.26   0.025     .0383784    .5569235
         2009  |   .3168169    .130902     2.42   0.016     .0594959    .5741379
         2010  |    .327644   .1337855     2.45   0.015     .0645962    .5906917
               |
        region |
            2  |   .1454559   .1476773     0.98   0.325    -.1444888    .4354006
            3  |   .4033952   .1502075     2.69   0.007     .1084415    .6983489
            4  |   .1663991   .1525094     1.09   0.276    -.1329311    .4657293
            5  |   .0408367   .1408036     0.29   0.772    -.2355438    .3172172
            6  |    .266744   .1747772     1.53   0.127    -.0762988    .6097869
            7  |   .4234858   .0977337     4.33   0.000     .2315778    .6153939
            8  |   .4222507   .1323493     3.19   0.001     .1623483    .6821532
            9  |   .5625251   .1225874     4.59   0.000     .3216061    .8034441
           10  |   .4505017    .133202     3.38   0.001     .1889558    .7120476
               |
         _cons |    4.05999   .5210219     7.79   0.000     3.031805    5.088174
--------------------------------------------------------------------------------

. 
. 
. *     ****************************************************************  *
. *       Generate the values of Z for which you want to calculate the    *
. *       marginal effect (and standard errors) of X on Y.                *
. *     ****************************************************************  *
. 
. generate MV=(-1*(_n-1)/1)

. 
. replace  MV=. if _n>81
(102,021 real changes made, 102,021 to missing)

. 
. *     ****************************************************************  *
. *       Grab elements of the coefficient and variance-covariance matrix *
. *       that are required to calculate the marginal effect and standard *
. *       errors.                                                         *
. *     ****************************************************************  *
. 
. matrix b=e(b) 

. matrix V=e(V)

.  
. scalar b1=b[1,2] 

. scalar b2=b[1,3]

. scalar b3=b[1,4]

. 
. 
. scalar varb1=V[2,2] 

. scalar varb2=V[3,3] 

. scalar varb3=V[4,4]

. 
. scalar covb1b3=V[2,4] 

. scalar covb2b3=V[3,4]

. 
. scalar list b1 b2 b3 varb1 varb2 varb3 covb1b3 covb2b3
        b1 = -.02633272
        b2 =  .00951189
        b3 = -.00080222
     varb1 =  .00003491
     varb2 =  .00001833
     varb3 =  3.655e-08
   covb1b3 =  9.771e-07
   covb2b3 = -5.053e-07

. 
. 
. *     ****************************************************************  *
. *       Calculate the marginal effect of X on Y for all MV values of    *
. *       the modifying variable Z.                                       *
. *     ****************************************************************  *
. 
. gen conb=b1+b3*MV if _n<82
(102,021 missing values generated)

. 
. 
. *     ****************************************************************  *
. *       Calculate the standard errors for the marginal effect of X on Y *
. *       for all MV values of the modifying variable Z.                  *
. *     ****************************************************************  *
. 
. gen conse=sqrt(varb1+varb3*(MV^2)+2*covb1b3*MV) if _n<82
(102,021 missing values generated)

. 
. 
. *     ****************************************************************  *
. *       Generate upper and lower bounds of the confidence interval.     *
. *       Specify the significance of the confidence interval.            *
. *     ****************************************************************  *
. 
. gen a=1.96*conse
(102,021 missing values generated)

.  
. gen upper=conb+a
(102,021 missing values generated)

.  
. gen lower=conb-a
(102,021 missing values generated)

. 
. *     ****************************************************************  *
. *       Graph the marginal effect of X on Y across the desired range of *
. *       the modifying variable Z.  Show the confidence interval.        *
. *     ****************************************************************  *
. 
. graph twoway line conb   MV, clwidth(medium) clcolor(blue) clcolor(black) ||   line upper  MV, clpattern(dash) clwidth(thin) clcolor(black
> ) ||   line lower  MV, clpattern(dash) clwidth(thin) clcolor(black) ||   , ylabel(-0.06 -0.04 -0.02 0 0.02 0.04 0.06,   labsize(2.5)) xlab
> el(-80 -60 -40 -20 0, labsize(2.5))   yscale(noline) xscale(noline) legend(col(1) order(1 2) label(1 "Marginal Effect of % Wom. in Parliam
> ent") label(2 "95% Confidence Interval") label(3 " ")) yline(0, lcolor(black)) title("Marginal Effect of % Women in Parliament" "on Corrup
> tion As Press Freedom Increases", size(4)) subtitle(" " "Dependent Variable: TI Corruption Perception Index" " ", size(3)) xtitle( "Press 
> Freedom", size(3)  ) xsca(titlegap(2)) ysca(titlegap(2)) ytitle("Marginal Effect of Women in Parliament", size(3)) scheme(s2mono) graphreg
> ion(fcolor(white)) yline(0.06, lcolor(gs15))

. 
. graph export ti-me-press.emf, replace
(file C:\Users\justi\Desktop\replication file\ti-me-press.emf written in Enhanced Metafile format)

. 
. drop MV conb conse a upper lower

. 
. 
. log close
      name:  <unnamed>
       log:  C:\Users\justi\Desktop\replication file\esarey-schwbay.log
  log type:  text
 closed on:  22 Jun 2016, 20:15:52
--------------------------------------------------------------------------------------------------------------------------------------------
